Battery Cell Diagnosing Apparatus and Method

ABSTRACT

Disclosed is a battery cell diagnosing apparatus, which includes a current measuring unit configured to measure a current of a battery cell; a voltage sensing unit configured to sense a cell voltage of the battery cell; and a first control unit configured to transmit first information of the battery cell including data obtained from the current measuring unit and the voltage sensing unit to an external device, receive second information including diagnostic information of the battery cell obtained based on the first information from the external device, and diagnose an abnormal state of the battery cell based on the first information and the second information.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 18/200,158 filed May 22, 2023, which is claims priority toKorean Patent Application Nos. 10-2022-0065020, 10-2022-0065021 and10-2022-0065022 filed on May 26, 2022 in the Republic of Korea, thedisclosures of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a battery cell diagnosing apparatusand method, and more particularly, to a battery cell diagnosingapparatus and method for diagnosing a state of a battery cell.

BACKGROUND ART

Recently, there is dramatically growing demand for portable electronicproducts such as laptop computers, video cameras and mobile phones, andwith the intense development of electric vehicles, accumulators forenergy storage, robots and satellites, many studies are being made onhigh performance batteries that can be recharged repeatedly.

Currently, commercially available batteries include nickel-cadmiumbatteries, nickel-hydrogen batteries, nickel-zinc batteries, lithiumbatteries and the like, and among them, lithium batteries have little orno memory effect, and thus they are gaining more attention thannickel-based batteries for their advantages of free charging anddischarging, a very low self-discharge rate and high energy density.

Recently, as applications requiring high voltage (e.g., an electricvehicle, an energy storage system) have become widespread, in somecases, a battery used in the electric vehicle or the energy storagesystem (ESS) ignites during use.

Accordingly, the need for a diagnostic technology for accuratelydetecting abnormalities of a plurality of battery cells connected in abattery pack is increasing.

DISCLOSURE Technical Problem

The present disclosure is designed to solve the problems of the relatedart, and therefore the present disclosure is directed to providing anapparatus and method for efficiently diagnosing an abnormal state of abattery cell by linking an on-board diagnostic device and an off-boarddiagnostic device.

These and other objects and advantages of the present disclosure may beunderstood from the following detailed description and will become morefully apparent from the exemplary embodiments of the present disclosure.Also, it will be easily understood that the objects and advantages ofthe present disclosure may be realized by the means shown in theappended claims and combinations thereof.

Technical Solution

A battery cell diagnosing apparatus according to an embodiment of thepresent disclosure comprises a current measuring unit configured tomeasure a current of a battery cell; a voltage sensing unit configuredto sense a cell voltage of the battery cell; and a first control unitconfigured to transmit first information of the battery cell includingdata obtained from the current measuring unit and the voltage sensingunit to an external device, receive second information includingdiagnostic information of the battery cell obtained based on the firstinformation from the external device, and diagnose an abnormal state ofthe battery cell based on the first information and the secondinformation.

The diagnostic information of the battery cell may include at least oneinformation among lithium precipitation diagnosis of the battery cell,abnormality of a parallel connection of the battery cell, and aninternal short circuit of the battery cell.

The first control unit may be configured to display information about anabnormal state of the battery cell based on the diagnostic informationof the battery cell included in the second information on a displayunit.

The first control unit may be configured to: detect at least one of avoltage abnormality of the battery cell and a behavior abnormality ofthe battery cell based on the first information, and diagnose anabnormal state of the battery cell based on at least one of the voltageabnormality, the behavior abnormality, and the second information.

The first control unit may be configured to generate third informationrepresenting whether the battery cell is in the abnormal state based onat least one of the voltage abnormality, the behavior abnormality, andthe second information.

The first control unit may be configured to display the thirdinformation on a display unit.

The first control unit may be configured to transmit the thirdinformation to a second control unit of a device equipped with thebattery cell.

The first control unit may be configured to: generate time series datarepresenting a history of the cell voltage included in the firstinformation over time, determine a first average cell voltage and asecond average cell voltage of each battery cell based on the timeseries data, the first average cell voltage being a short-term movementaverage, the second average cell voltage being a long-term movementaverage, and detect a voltage abnormality of the battery cell based on adifference between the first average cell voltage and the second averagecell voltage.

The battery cell diagnosing apparatus may be configured to diagnose aplurality of battery cells.

The first control unit may be configured to: for each of the pluralityof battery cells, determine a long-term and short-term averagedifference corresponding to the difference between the first averagecell voltage and the second average cell voltage, determine an averagevalue of the long-term and short-term average differences of theplurality of battery cells, for each of the plurality of battery cells,determine a cell diagnosis deviation corresponding to a deviation of theaverage value of the long-term and short-term average differences andthe long-term and short-term average difference, and detect a batterycell that satisfies a condition in which the cell diagnosis deviationexceeds a diagnosis threshold as a voltage abnormal cell.

In another aspect of the present disclosure, the battery cell diagnosingapparatus may be configured to diagnose a plurality of battery cells.

The first control unit may be configured to: for each of the pluralityof battery cells, determine a long-term and short-term averagedifference corresponding to the difference between the first averagecell voltage and the second average cell voltage, determine an averagevalue of the long-term and short-term average differences of theplurality of battery cells, for each of the plurality of battery cells,determine a cell diagnosis deviation corresponding to a deviation of theaverage value of the long-term and short-term average differences andthe long-term and short-term average difference, determine astatistically variable threshold that depends on a standard deviation ofthe cell diagnosis deviations of the plurality of battery cells, filterthe time series data based on the statistically variable threshold togenerate filtered time series data; and detect a voltage abnormality ofthe battery cell based on the time or number of data of the filteredtime series data exceeding a diagnosis threshold.

The first control unit may be configured to: for each of the pluralityof battery cells, determine a long-term and short-term averagedifference corresponding to the difference between the first averagecell voltage and the second average cell voltage, determine anormalization value corresponding to an average value of the long-termand short-term average differences of the plurality of battery cells,for each of the plurality of battery cells, normalize the long-term andshort-term average difference according to the normalization value,determine a statistically variable threshold that depends on a standarddeviation of the normalized cell diagnosis deviations of the pluralityof battery cells, for each of the plurality of battery cells, filter thenormalized long-term and short-term average difference of each batterycell based on the statistically variable threshold to generate filteredtime series data; and detect a voltage abnormality of the battery cellbased on the time or number of data of the filtered time series dataexceeding a diagnosis threshold.

The first control unit may be configured to: determine a plurality ofsub voltage curves by applying a moving window of a first time length toa time series of the cell voltage included in the first information,determine a long-term average voltage value of each sub voltage curveusing a first average filter of the first time length, determine ashort-term average voltage value of each sub voltage curve using asecond average filter of a second time length shorter than the firsttime length, determine a voltage deviation corresponding to a differencebetween the long-term average voltage value and the short-term averagevoltage value of each sub voltage curve, and compare each of theplurality of voltage deviations determined for the plurality of subvoltage curves with at least one of a first threshold deviation and asecond threshold deviation in order to detect the behavior abnormalityof the battery cell.

The first control unit may be configured to detect the behaviorabnormality corresponding to two voltage deviations that respectivelysatisfy a first condition, a second condition and a third conditionamong the plurality of voltage deviations.

The first condition may be satisfied when a first voltage deviation ofthe two voltage deviations is equal to or greater than the firstthreshold deviation.

The second condition may be satisfied when a second voltage deviation ofthe two voltage deviations is equal to or less than the second thresholddeviation.

The third condition may be satisfied when a time interval between thetwo voltage deviations is equal to or less than a threshold time.

The second information may represent whether an accumulated capacitydifference change amount is greater than or equal to a threshold value,and the accumulated capacity difference change amount is the sum ofcapacity difference change amounts.

Each of the capacity difference change amounts may be a differencebetween a capacity difference of a k^(th) charging and discharging cycleof the battery cell and a capacity difference of a k−1^(th) charging anddischarging cycle of the battery cell, and the k may be a natural numbergreater than or equal to 2.

The capacity difference of each charging and discharging cycle maycorrespond to a difference between a charging capacity of the batterycell during a charging process of the charging and discharging cycle anda discharging capacity of the battery cell during a discharging processof the charging and discharging cycle.

Each of the charging capacity and the discharging capacity may bederivable from data obtained from the current measuring unit andincluded in the first information.

The second information may represent a capacity difference change amountbetween successive charging and discharging cycles of the battery cell.

A capacity difference for each charging and discharging cycle of thebattery cell may be a difference between (i) a charging capacity of thebattery cell during a charging process of the charging and dischargingcycle of the battery cell and (ii) a discharging capacity of the batterycell during the discharging process of the charging and dischargingcycle of the battery cell.

The second information may represent whether a parallel connection of aplurality of unit cells included in the battery cell is abnormal basedon a result of monitoring a change over time of an estimated capacityvalue by the external device.

The estimated capacity value may represent a full charging capacity ofthe battery cell based on charging and discharging data.

The charging and discharging data may include a voltage time seriesrepresenting the change over time of the voltage of the battery cell anda current time series representing the change over time of the chargingand discharging current of the battery cell.

The second information may represent whether the battery cell has aninternal short circuit based on a first SOC change and a criterionfactor of the battery cell.

The criterion factor may be determined by applying a statisticalalgorithm to the first SOC change of at least two battery cells among aplurality of battery cells.

The first SOC change may be a difference between a first SOC at a firstcharging time point of each battery cell and a second SOC at a secondcharging time point.

The first SOC may be estimated by applying a SOC estimation algorithm toa state parameter of the battery cell at the first charging time point.

The second SOC may be estimated by applying the SOC estimation algorithmto the state parameter of the battery cell at the second charging timepoint.

The state parameter may be obtained based on the first information.

In another aspect of the present disclosure, there is also provided abattery cell diagnosing system, comprising the battery cell diagnosingapparatus according to an aspect of the present disclosure.

The external device may be configured to derive the second informationbased on at least a part of the first information.

In still another aspect of the present disclosure, there is alsoprovided a battery cell diagnosing method, comprising: by a controlunit, obtaining data including at least one of a charging current and adischarging current of a battery cell, and a cell voltage of the batterycell; by the control unit, transmitting first information of the batterycell including the obtained data to an external device; by the controlunit, receiving second information including diagnostic information ofthe battery cell obtained based on the first information from theexternal device; and by the control unit, diagnosing an abnormal stateof the battery cell based on the first information and the secondinformation.

In still another aspect of the present disclosure, the battery celldiagnosing method may further comprise: by the control unit, detectingat least one of a voltage abnormality and a behavior abnormality of thebattery cell based on the first information of the battery cell, and bythe control unit, diagnosing an abnormal state of the battery cell basedon at least one of the voltage abnormality of the battery cell, thebehavior abnormality of the battery cell, and the second information.

Advantageous Effects

According to at least one of the embodiments of the present disclosure,an abnormal state of a battery cell can be efficiently diagnosed bylinking an on-board device and an off-board device.

According to at least one of the embodiments of the present disclosure,software resources and time required for diagnosing an abnormality ofeach battery cell can be saved by linking the on-board device and theoff-board device, and the possibility of a false diagnosis caused as thenumber of abnormal battery cells increases among a plurality of batterycells may decrease.

According to at least one of the embodiments of the present disclosure,both a long-term trend and a short-term trend of the cell voltage ofeach battery cell may be considered, so that an abnormal change of thecorresponding battery cell may be precisely detected.

DESCRIPTION OF DRAWINGS

The accompanying drawings illustrate a preferred embodiment of thepresent disclosure and together with the foregoing disclosure, serve toprovide further understanding of the technical features of the presentdisclosure, and thus, the present disclosure is not construed as beinglimited to the drawing.

FIG. 1 is an exemplary diagram showing a system including a battery celldiagnosing apparatus according to an embodiment of the presentdisclosure.

FIG. 2 is a block diagram schematically showing the functionalconfiguration of the battery cell diagnosing apparatus according to anembodiment of the present disclosure.

FIG. 3 is an exemplary diagram conceptually illustrating theconfiguration of an electric vehicle according to an embodiment of thepresent disclosure.

FIGS. 4 a to 4 h are graphs exemplarily illustrating a process ofdetecting a voltage abnormality of each battery cell from time seriesdata representing a change in cell voltage of each of the plurality ofbattery cells illustrated in FIG. 3 .

FIG. 5 is a graph exemplarily showing a voltage curve corresponding to araw time series of actual voltage values of a cell voltage of a batterycell referenced in various embodiments of the present disclosure.

FIG. 6 is a graph exemplarily showing a measured voltage curve obtainedby synthesizing the measured noise with the raw time seriescorresponding to the voltage curve of FIG. 5 .

FIG. 7 is a graph exemplarily showing a first movement average curveobtained by applying a first average filter to the voltage curve of FIG.6 .

FIG. 8 is a graph exemplarily showing a second movement average curveobtained by applying a second average filter to the voltage curve ofFIG. 6 .

FIG. 9 is a graph exemplarily showing a voltage deviation curve that isa difference between the first movement average curve of FIG. 7 and thesecond movement average curve of FIG. 8 .

FIG. 10 is a block diagram showing a schematic configuration of anexternal device according to an embodiment of the present disclosure.

FIG. 11 is a diagram exemplarily showing a schematic configuration of abattery cell referred to in various embodiments of the presentdisclosure.

FIG. 12 is a diagram for illustrating a first capacity abnormality(incomplete disconnection failure) of a battery cell referred to invarious embodiments of the present disclosure.

FIG. 13 is a diagram for illustrating a second capacity abnormality(complete disconnection failure) of a battery cell referred to invarious embodiments of the present disclosure.

FIG. 14 is an exemplary diagram for illustrating a relationship betweena capacity abnormality and a full charging capacity of a battery cellreferred to in various embodiments of the present disclosure.

FIG. 15 is a reference diagram for illustrating an exemplary equivalentcircuit of a battery cell referred to in various embodiments of thepresent disclosure.

FIG. 16 is an exemplary graph for comparing SOC changes of a batterycell according to the presence or absence of an internal short circuitabnormality referred to in various embodiments of the presentdisclosure.

FIG. 17 is another exemplary graph for comparing SOC changes of abattery cell according to the presence or absence of an internal shortcircuit abnormality referred to in various embodiments of the presentdisclosure.

FIG. 18 is still another exemplary graph for comparing SOC changes of abattery cell according to the presence or absence of an internal shortcircuit abnormality referred to in various embodiments of the presentdisclosure.

FIG. 19 is a flowchart in which the battery cell diagnosing apparatusaccording to an embodiment of the present disclosure diagnoses anabnormal state of a battery cell using the external device.

FIG. 20 is a flowchart exemplarily showing a method for detectingvoltage abnormality according to an embodiment of the presentdisclosure.

FIG. 21 is another flowchart exemplarily showing a method of detectingvoltage abnormality according to an embodiment of the presentdisclosure.

FIG. 22 is another flowchart exemplarily showing a method of detectingvoltage abnormality according to an embodiment of the presentdisclosure.

FIG. 23 is still another flowchart exemplarily showing a method ofdetecting voltage abnormality according to an embodiment of the presentdisclosure.

FIG. 24 is still another flowchart exemplarily showing a method ofdetecting voltage abnormality according to an embodiment of the presentdisclosure.

FIG. 25 is a flowchart exemplarily showing a method of detectingbehavior abnormality according to an embodiment of the presentdisclosure.

FIG. 26 is another flowchart exemplarily showing a method of detectingbehavior abnormality according to an embodiment of the presentdisclosure.

FIG. 27 is a flowchart exemplarily showing a method of detecting lithiumprecipitation abnormality according to an embodiment of the presentdisclosure.

FIG. 28 is another flowchart exemplarily showing a method of detectinglithium precipitation abnormality according to an embodiment of thepresent disclosure.

FIG. 29 is still another flowchart exemplarily showing a method ofdetecting lithium precipitation abnormality according to an embodimentof the present disclosure.

FIG. 30 is still another flowchart exemplarily showing a method ofdetecting lithium precipitation abnormality according to an embodimentof the present disclosure.

FIG. 31 is a graph showing changes in data measured in an experimentalexample to which a method for the external device to detect whetherlithium precipitation according to an embodiment of the presentdisclosure occurs is applied.

FIG. 32 is a graph showing changes in data measured in anotherexperimental example to which a method for detecting lithiumprecipitation abnormality according to an embodiment of the presentdisclosure is applied.

FIG. 33 is a flowchart in which the battery cell diagnosing apparatusaccording to an embodiment of the present disclosure diagnoses anabnormal state of a battery cell using an external device.

FIG. 34 is a flowchart exemplarily showing a battery diagnosing methodaccording to an embodiment of the present disclosure.

FIG. 35 is a flowchart in which the battery cell diagnosing apparatusaccording to an embodiment of the present disclosure diagnoses anabnormal state of a battery cell using an external device.

FIG. 36 is a flowchart exemplarily showing a battery management methodaccording to an embodiment of the present disclosure.

FIG. 37 is another flowchart exemplarily showing a battery managementmethod according to an embodiment of the present disclosure.

BEST MODE

Hereinafter, preferred embodiments of the present disclosure will bedescribed in detail with reference to the accompanying drawings. Priorto the description, it should be understood that the terms used in thespecification and the appended claims should not be construed as limitedto general and dictionary meanings, but interpreted based on themeanings and concepts corresponding to technical aspects of the presentdisclosure on the basis of the principle that the inventor is allowed todefine terms appropriately for the best explanation.

Therefore, the description proposed herein is just a preferable examplefor the purpose of illustrations only, not intended to limit the scopeof the disclosure, so it should be understood that other equivalents andmodifications could be made thereto without departing from the scope ofthe disclosure.

FIG. 1 is an exemplary diagram showing a battery cell diagnosing system1 including a battery cell diagnosing apparatus 1000.

Referring to FIG. 1 , the battery cell diagnosing system 1 may beconfigured to include a battery cell diagnosing apparatus 1000 and anexternal device 2000. However, this is only a preferred embodiment forachieving the present disclosure, and some components may be added ordeleted as needed. It should be noted that the components of the batterycell diagnosing system 1 shown in FIG. 1 represents functionallydistinct functional elements, and a plurality of components may beimplemented to be integrated with each other in an actual physicalenvironment.

In the battery cell diagnosing system 1, the battery cell diagnosingapparatus 1000 is a computing device that diagnoses an abnormal state ofa battery cell and provides a diagnosed result to a user. The batterycell diagnosing apparatus 1000 may refer to an on-board computing deviceincluded in the BMS (Battery Management System). For example, thebattery cell diagnosing apparatus 1000 may be a computing deviceincluded in a BMS provided in a user's electric vehicle. This is anexample, and the present disclosure is not limited thereto but mayinclude all kinds of devices equipped with computing functions andcommunication functions.

The battery cell diagnosing apparatus 1000 may obtain data including atleast one of a charging current and a discharging current of the batterycell, and a cell voltage that is a voltage across both ends of thebattery cell. For example, the battery cell diagnosing apparatus 1000may obtain data on at least one of a charging current and a dischargingcurrent of a battery cell provided in an electric vehicle, and a cellvoltage that is a voltage across both ends of the battery cell.

According to an embodiment of the present disclosure, the battery celldiagnosing apparatus 1000 may generate first information of the batterycell. The first information may include data related to at least one ofthe charging current and the discharging current of the battery cell,and the cell voltage that is a voltage across both ends of the batterycell.

The battery cell diagnosing apparatus 1000 may transmit the firstinformation to the external device 2000. The external device 2000 mayreceive the first information and derive second information about thebattery cell based on at least a part of the received first information.The external device 2000 may transmit the second information to thebattery cell diagnosing apparatus 1000. The battery cell diagnosingapparatus 1000 may diagnose an abnormal state of the battery cell basedon the first information and the second information.

In the battery cell diagnosing system 1, the external device 2000 maymean an off-board computing device that provides the second informationgenerated using the first information to the battery cell diagnosingapparatus 1000. To this end, the external device 2000 may receiveinformation about voltage, current, or temperature of the battery cellfrom various electric vehicles, and store at least one algorithm capableof diagnosing the battery cell based on the received information. Inaddition, the external device 2000 may store the information about thevoltage, current, or temperature of the battery cell received fromvarious electric vehicles as big data, and store at least one artificialintelligence model capable of diagnosing the battery cell based on thebig data. The computing device may be a notebook, a desktop, a laptop,etc., but is not limited thereto and may include any type of deviceequipped with a computing function and a communication function.However, if the second information is provided to a plurality ofapparatuses 1000 for diagnosing a battery cell, the external device 2000may be preferably implemented as a server computing device.

The components of the battery cell diagnosing system 1 may communicatethrough a network. Here, the network may be implemented as all types ofwired/wireless networks such as local area network (LAN), wide areanetwork (WAN), mobile radio communication network, Wibro (WirelessBroadband Internet), etc.

So far, the battery cell diagnosing system 1 according to an embodimentof the present disclosure has been described with reference to FIG. 1 .Hereinafter, the battery cell diagnosing apparatus 1000 according to anembodiment of the present disclosure will be described in detail withreference to FIG. 2 .

FIG. 2 is a block diagram schematically showing the functionalconfiguration of the battery cell diagnosing apparatus 1000 according toan embodiment of the present disclosure. Referring to FIG. 2 , thebattery cell diagnosing apparatus 1000 may include a current measuringunit 100, a voltage sensing unit 200, a data obtaining unit 300, a firstcontrol unit 400 and a display unit 500.

The current measuring unit 100 may measure a current of the batterycell. Here, the current may be at least one of a charging current and adischarging current of the battery cell.

Preferably, the current measuring unit 100 may measure the chargingcurrent while the battery cell is being charged. In addition, thecurrent measuring unit 100 may measure the discharging current while thebattery cell is being discharged.

The voltage sensing unit 200 may be configured to sense a cell voltageof the battery cell. For example, the voltage sensing unit 200 may sensea voltage signal representing a cell voltage that is a voltage acrossboth ends of a battery cell. This will be described later in detail withreference to FIG. 3 .

The data obtaining unit 300 may periodically obtain data from thecurrent measuring unit 100 and the voltage sensing unit 200.

In an embodiment, the first control unit 400 may generate firstinformation of the battery cell based on the data obtained by the dataobtaining unit 300. For example, the first information may includecurrent information about at least one of the charging current and thedischarging current of the battery cell and voltage information aboutthe cell voltage of the battery cell.

In another embodiment, the first control unit 400 may directly obtaincurrent information about the battery cell from the current measuringunit 100 and directly obtain voltage information about the battery cellfrom the voltage sensing unit 200.

The first control unit 400 may be configured to transmit the generatedfirst information to the external device 2000. Also, the first controlunit 400 may receive second information including diagnostic informationof the battery cell obtained based on the first information from theexternal device 2000.

Specifically, the second information is diagnostic information of thebattery cell generated by the external device 2000 based on the firstinformation. For example, the diagnostic information of the battery cellmay include at least one information among a lithium precipitationdiagnosis of the battery cell, a parallel connection abnormality of thebattery cell, or an internal short circuit of the battery cell.

The first control unit 400 may be configured to diagnose an abnormalstate of the battery cell based on the first information and the secondinformation.

Specifically, the first control unit 400 may detect at least one of avoltage abnormality of the battery cell and a behavior abnormality ofthe battery cell based on the first information. Also, the first controlunit 400 may be configured to diagnose an abnormal state of the batterycell based on at least one of the voltage abnormality, the behaviorabnormality, and the second information. As such, the present disclosureis characterized in that the battery cell diagnosing apparatus 1000 andthe external device 2000 diagnose different diagnostic items, ratherthan diagnosing the same diagnostic item. For example, the battery celldiagnosing apparatus 1000 may diagnose at least one of the voltageabnormality and the behavior abnormality of the battery cell, and theexternal device 2000 may diagnose the lithium precipitation of thebattery cell, the parallel connection abnormality of the battery cell,and the internal short circuit of the battery cell.

The feature that the first control unit 400 detects a voltageabnormality or a behavior abnormality of the battery cell based on thefirst information will be described later.

The display unit 500 may include at least one display. The display unit500 may display information about the abnormal state of the battery cellon the included display.

Here, the display unit 500 may be electrically connected to the firstcontrol unit 400 and may be included in a load device receiving powerfrom a cell group CG. When the load device is an electric vehicle, ahybrid electric vehicle, a plug-in hybrid vehicle, or the like, thediagnosis result information may be output through an integratedinformation display of the vehicle.

For example, the first control unit 400 may be configured to display onthe display unit 500 the information about an abnormal state of thebattery cell based on the diagnostic information of the battery cellincluded in the second information.

As another example, the first control unit 400 may generate thirdinformation representing whether the battery cell is in an abnormalstate based on at least one of the voltage abnormality, the behaviorabnormality, and the second information. In addition, the first controlunit 400 may display the third information using the display included inthe display unit 500. By displaying the third information by the firstcontrol unit 400 using the display included in the display unit 500, theabnormality of the battery cell may be specifically provided to theuser.

In addition, the first control unit 400 may transmit the thirdinformation to a second control unit of a device equipped with thebattery cell.

Here, the second control unit may be configured to perform a function ofcontrolling the device equipped with a battery cell. For example, thedevice equipped with a battery cell may be an electric vehicle. In thiscase, the second control unit may be an ECU (Electronic Control Unit)configured to control the electric vehicle. The first control unit 400may transmit the third information to the second control unit of theelectric vehicle equipped with the battery cell.

FIG. 3 is an exemplary diagram conceptually illustrating theconfiguration of an electric vehicle according to an embodiment of thepresent disclosure. Referring to FIG. 3 , the electric vehicle includesa battery pack 10, an inverter INV, an electric motor M, and a secondcontrol unit 600.

The battery pack 10 may include a cell group CG, a switch S, and thebattery cell diagnosing apparatus 1000.

The cell group CG may be coupled to the inverter INV through a pair ofpower terminals provided in the battery pack 10. The cell group CGincludes a plurality of battery cells BC₁ to BC_(N) connected in series(where N is a natural number equal to or greater than 2). The type ofeach battery cell BC_(i) is not particularly limited as long as it canbe recharged like a lithium-ion battery cell. i is an index for cellidentification. i is a natural number from 1 to N.

The switch S is connected in series to the cell group CG. The switch Sis installed on a current path for charging and discharging the cellgroup CG. The switch S is controlled to turn on/off in response to aswitching signal from the battery cell diagnosing apparatus 1000.Preferably, the operating state of the switch S may be controlled by thefirst control unit 400 as a turn-on state or a turn-off state.

For example, the switch S may be a mechanical relay turned on/off by themagnetic force of the coil. As another example, the switch S may be asemiconductor switch such as a Field Effect Transistor (FET) or a MetalOxide Semiconductor Field Effect Transistor (MOSFET).

The inverter INV is provided to convert a DC current from the cell groupCG into an AC current in response to a command from the battery celldiagnosing apparatus 1000.

The electric motor M may be, for example, a three-phase AC motor. Theelectric motor M is driven using AC power from the inverter INV.

The battery cell diagnosing apparatus 1000 is provided to take charge ofoverall control related to charging and discharging of the cell groupCG.

The battery cell diagnosing apparatus 1000 may further include at leastone of a temperature sensor T and an interface unit I/F.

The voltage sensing unit 200 is connected to positive and negativeelectrodes of each of the plurality of battery cells BC₁ to BC_(N)through a plurality of voltage sensing lines. The voltage sensing unit200 is configured to measure a cell voltage across both ends of eachbattery cell BC_(i) and generate a voltage signal representing themeasured cell voltage.

The current measuring unit 100 is connected in series to the cell groupCG through the current path. The current measuring unit 100 isconfigured to detect a battery current flowing through the cell group CGand generate a current signal representing the detected battery current(which may also be referred to as ‘charging and discharging current’).Since the plurality of battery cells BC₁ to BC_(N) are connected inseries, the battery current flowing through any one of the plurality ofbattery cells BC₁ to BC_(N) may be the same as the battery currentflowing through the other battery cells. The current measuring unit 100may be implemented using one or a combination of two or more of knowncurrent detection elements such as a shunt resistor and a Hall Effectelement.

The temperature sensor T is configured to detect the temperature of thecell group CG and generate a temperature signal indicating the detectedtemperature. For example, the temperature sensor T may measure thetemperature of the cell group CG or may individually measure thetemperature of each battery cell BC_(i) included in the cell group CG.

The first control unit 400 may be operatively coupled to the voltagesensing unit 200, the temperature sensor T, the current measuring unit100, the interface unit I/F, and/or the switch S. The first control unit400 may collect a sensing signal from the voltage sensing unit 200, thecurrent measuring unit 100, and the temperature sensor T. The sensingsignal refers to a voltage signal, a current signal, and/or atemperature signal synchronously detected.

The interface unit I/F may include a communication circuit configured tosupport wired communication or wireless communication between the firstcontrol unit 400 and the second control unit 600. For example, the wiredcommunication may be CAN (Controller area network) and/or CAN-FD(Controller area network with flexible data rate) communication, and thewireless communication may be ZigBee or Bluetooth communication. Ofcourse, as long as wired/wireless communication between the firstcontrol unit 400 and the second control unit 600 is supported, the typeof communication protocol is not particularly limited.

The interface unit I/F may be coupled with an output device (e.g., adisplay, a speaker) that provides the information received from thesecond control unit 600 and/or the first control unit 400 in auser-recognizable form.

The second control unit 600 may control the inverter INV based on thebattery information (e.g., voltage, current, temperature, SOC) collectedthrough communication with the battery cell diagnosing apparatus 1000.

When the switch S is turned on while an electric load and/or a chargeris operating, the battery cells BC₁ to BC_(N) included in the batterypack 10 may be charged or discharged. When the switch S is turned offwhile the battery cells BC₁ to BC_(N) are charging or discharging, thebattery cells BC₁ to BC_(N) may be switched to an idle state.

The first control unit 400 may turn on the switch S in response to akey-on signal. The first control unit 400 may turn off the switch S inresponse to a key-off signal. The key-on signal is a signal thatrequests transition from an idle state to a charging or dischargingstate. The key-off signal is a signal that requests transition from acharging or discharging state to an idle state. Alternatively, theon/off control of the switch S may be performed by the second controlunit 600 instead of the first control unit 400.

In FIG. 3 , the battery cell diagnosing apparatus 1000 is illustrated asbeing included in the battery pack 10 for an electric vehicle, but thisshould be understood as an example. For example, the battery celldiagnosing apparatus 1000 may be included in a test system used toselect a behavior abnormal battery cell in a manufacturing process ofthe battery cell BC₁ to BC_(N). As another example, the battery celldiagnosing apparatus 1000 may also be included in an energy storagesystem (ESS) including battery cells BC₁ to BC_(N).

The first control unit 400 may detect voltage abnormality and behaviorabnormality of the battery cell by using the first information. First, amethod in which the first control unit 400 detects voltage abnormalityof a battery cell using the first information will be described indetail with reference to FIG. 4 .

FIGS. 4 a to 4 h are graphs exemplarily illustrating a process ofdetecting a voltage abnormality of each battery cell from time seriesdata representing a change in cell voltage of each of the plurality ofbattery cells BC₁ to BC_(N) illustrated in FIG. 3 .

FIG. 4 a shows a voltage curve of each of the plurality of battery cellsBC₁ to BC_(N). The number of battery cells shown in FIG. 4 a is 14. Thefirst control unit 400 collects the voltage signal from the voltagesensing unit 200 every unit time, and records the voltage value of thecell voltage of each battery cell BC_(i) in the first information. Theunit time may be an integer multiple of the voltage measurement periodof the voltage sensing unit 200.

The first control unit 400 may be configured to generate time seriesdata representing a history over time of the cell voltage included inthe first information.

Specifically, the first control unit 400 may generate cell voltage timeseries data representing a history of the cell voltage of each batterycell over time based on the voltage value of the cell voltage of eachbattery cell BC_(i) included in the first information. Whenever eachtime cell voltage is measured, the number of cell voltage time seriesdata increases by 1.

The plurality of voltage curves shown in FIG. 4 a are one-to-one relatedto the plurality of battery cells BC₁ to BC_(N). Therefore, each voltagecurve represents the change history of the cell voltage of any onebattery cell BC associated therewith.

The first control unit 400 may be configured to determine a firstaverage cell voltage and a second average cell voltage of each batterycell based on the time series data. Here, the first average cell voltagemay be a short-term moving average, and the second average cell voltagemay be a long-term moving average.

Specifically, the first control unit 400 may determine a moving averageof each of the plurality of battery cells BC₁ to BC_(N) for each unittime by using one moving window or two moving windows. When using twomoving windows, the time length for one moving window is different fromthe time length for the other moving window.

Here, the time length of each moving window is an integer multiple ofthe unit time, an end point of each moving window is the current timepoint, and a start point of each moving window is a point ahead of thecurrent time point by a predetermined time length.

Hereinafter, for convenience of description, among the two movingwindows, one associated with a shorter time length will be referred toas a first moving window, and one associated with a longer time lengthwill be referred to as a second moving window.

The first control unit 400 may diagnose the voltage abnormality of eachbattery cell BC_(i) using only the first moving window or using both thefirst moving window and the second moving window.

The first control unit 400 may compare the short-term and long-termchange trends of the cell voltage of the i^(th) battery cell BC_(i)based on the cell voltage of the i^(th) battery cell BC_(i) collectedfor each unit time.

The first control unit 400 may determine the first average cell voltage,which is a moving average of the i^(th) battery cell BC_(i) by the firstmoving window, for each unit time by using Equation 1 or Equation 2below. That is, the first control unit 400 may determine the firstaverage cell voltage of each battery cell using the first moving window.

Equation 1 is a moving average calculation formula using an arithmeticaverage method, and Equation 2 is a moving average calculation formulausing a weighted average method.

$\begin{matrix}{{{SMA}_{i}\lbrack k\rbrack} = \frac{\sum_{j = 1}^{S}{V_{i}\left\lbrack {k - S + j} \right\rbrack}}{S}} & {< {{Equation}1} >}\end{matrix}$ $\begin{matrix}{{{SMA}_{i}\lbrack k\rbrack} = {\frac{{{SMA}_{i}\left\lbrack {k - 1} \right\rbrack} \times \left( {S - 1} \right)}{S} + \frac{V_{i}\lbrack k\rbrack}{S}}} & {< {{Equation}2} >}\end{matrix}$

In Equation 1 and Equation 2, k is a time index indicating the currenttime point, SMA_(i)[k] is the first average cell voltage of the i^(th)battery cell BC_(i) at the present time, S is the time length of thefirst moving window divided by the unit time, and V_(i)[k] is the cellvoltage of the i^(th) battery cell BC_(i) at the current time point. Forexample, if the unit time is 1 second and the time length of the firstmoving window is 10 seconds, S is 10. When x is a natural number lessthan or equal to k, V_(i)[k−x] and SMA_(i)[k−x] represent the cellvoltage of the i^(th) battery cell BC_(i) and the first average cellvoltage when the time index is k−x, respectively. For reference, thefirst control unit 400 may be set to increase the time index by 1 foreach unit time.

The first control unit 400 may determine the second average cellvoltage, which is a moving average of the i^(th) battery cell BC_(i) bythe second moving window, for each unit time by using Equation 3 orEquation 4 below. That is, the second control unit 400 may determine thesecond average cell voltage using the second moving window.

Equation 3 is a moving average calculation formula using an arithmeticaverage method, and Equation 4 is a moving average calculation formulausing a weighted average method.

$\begin{matrix}{{{{LMA}_{i}\lbrack k\rbrack} = \frac{\sum_{j = 1}^{L}{V_{i}\left\lbrack {k - L + j} \right\rbrack}}{L}}} & {< {{Equation}3} >}\end{matrix}$ $\begin{matrix}{{{LMA}_{i}\lbrack k\rbrack} = {\frac{{{LMA}_{i}\left\lbrack {k - 1} \right\rbrack} \times \left( {L - 1} \right)}{L} + \frac{V_{i}\lbrack k\rbrack}{L}}} & {< {{Equation}4} >}\end{matrix}$

In Equation 3 and Equation 4, k is a time index indicating the currenttime point, LMA_(i)[k] is the second average cell voltage of the i^(th)battery cell BC_(i) of the current time point, L is the time length ofthe second moving window divided by the unit time, and V_(i)[k] is thecell voltage of the i^(th) battery cell BC_(i) at the current timepoint. For example, if the unit time is 1 second and the time length ofthe second moving window is 100 seconds, L is 100. When x is a naturalnumber less than or equal to k, LMA_(i)[k−x] represents the secondaverage cell voltage when the time index is k−x.

In one embodiment, as V_(i)[k] of Equation 1 to 4, the first controlunit 400 may input the difference between the criterion cell voltage ofthe cell group CG at the current time point and the cell voltages of thebattery cell BC_(i), instead of the cell voltage of each battery cellBC_(i) at the current time point.

The criterion cell voltage of the cell group CG at the current timepoint is an average value of the plurality of cell voltages at thecurrent time point determined from the plurality of battery cells BC₁ toBC_(N). In a modification, the average value of the plurality of cellvoltages may be replaced by a median value.

Specifically, the first control unit 400 may set VD_(i)[k] of thefollowing Equation 5 to V_(i)[k] of Equations 1 to 4.

VD _(i) [k]=Vav[k]−V _(i) [k]  <Equation 5>

In Equation 5, Vav[k] is an average value of the plurality of cellvoltages as the criterion cell voltage of the cell group CG at thecurrent time point.

When the time length of the first moving window is shorter than the timelength of the second moving window, the first average cell voltage maybe called a ‘short-term movement average’ of the cell voltage, and thesecond average cell voltage may be called a ‘long-term movement average’of the cell voltage.

The first control unit may detect voltage abnormality of the batterycell based on the difference between the first average cell voltage andthe second average cell voltage. It will be described in detail withreference to FIG. 4 b.

The first control unit may determine, for each of the plurality ofbattery cells, a long-term and short-term average differencecorresponding to the difference between the first average cell voltageand the second average cell voltage of the battery cell.

FIG. 4 b shows a short-term movement average line and a long-termmovement average line for the cell voltage of the i^(th) battery cellBC_(i) determined from the plurality of voltage curves shown in FIG. 4 a. In FIG. 4 b , the horizontal axis represents time, and the verticalaxis represents a moving average value of the cell voltage.

Referring to FIG. 4 b , a plurality of moving average lines Si indicatedby dotted lines are one-to-one related to the plurality of battery cellsBC₁ to BC_(N), and represent the history of changes of the first averagecell voltage (SMA_(i)[k]) of each battery cell BC according to time. Inaddition, the plurality of moving average lines Li indicated by solidlines are one-to-one related to the plurality of battery cells BC₁ toBC_(N), and represent the history of changes of the second average cellvoltage (LMA_(i)[k]) of each battery cell BC according to time.

The dotted line graph and the solid line graph are obtained usingEquation 2 and Equation 4, respectively. In addition, VD_(i)[k] ofEquation 5 is used as V_(i)[k] of Equation 2 and Equation 4, and Vav[k]is set as an average of the plurality of cell voltages. The time lengthof the first moving window is 10 seconds and the time length of thesecond moving window is 100 seconds.

The first control unit 400 may determine an average value of thedetermined long-term and short-term average differences of the pluralityof battery cells.

In addition, for each of the plurality of battery cells, the firstcontrol unit 400 may determine the cell diagnosis deviationcorresponding to the average value of the long-term and short-termaverage difference of all battery cells and the deviation of thelong-term and short-term average difference of the battery cell. Forexample, the first control unit 400 may determine a cell diagnosisdeviation for each battery cell by calculating the average value of thelong-term and short-term average differences and the deviation of thelong-term and short-term average difference of each battery cell.

FIG. 4 c shows the change of the long-term and short-term averagedifference (absolute value) corresponding to the difference between thefirst average cell voltage (SMA_(i)[k]) and the second average cellvoltage (LMA_(i)[k]) of each battery cell shown in FIG. 4 b according totime. In FIG. 4 c , the horizontal axis represents time, and thevertical axis represents the long-term and short-term average differenceof each battery cell BC_(i).

The long-term and short-term average difference of each battery cellBC_(i) is the difference between the first average cell voltage SMA_(i)and the second average cell voltage LMA_(i) of each battery cell BC_(i)for each unit time. As an example, the long-term and short-term averagedifference of the i^(th) battery cell BC_(i) may be the same as thevalue obtained by subtracting the other (e.g., smaller one) from one(e.g., larger one) of SMA_(i)[k] and LMA_(i)[k].

The long-term and short-term average difference of the i^(th) batterycell BC_(i) depends on the short-term and long-term changes in the cellvoltage of the i^(th) battery cell BC_(i).

The temperature or SOH of the i^(th) battery cell BC_(i) continuouslyaffects the cell voltage of the i^(th) battery cell BC_(i) in the shortterm as well as in the long term. Therefore, if there is no voltageabnormality of the i^(th) battery cell BC_(i), the long-term andshort-term average difference of the i^(th) battery cell BC_(i) is notsignificantly different from the long-term and short-term averagedifference of the other battery cells.

On the other hand, voltage abnormality suddenly generated due to aninternal short circuit and/or an external short circuit in the i^(th)battery cell BC_(i) affects the first average cell voltage (SMA_(i)[k])more than the second average cell voltage (LMA_(i)[k]). As a result, thelong-term and short-term average difference of the i^(th) battery cellBC_(i) has a large deviation from the long-term and short-term averagedifference of the remaining battery cells without voltage abnormality.

The first control unit 400 may determine the long-term and short-termaverage difference (|SMA_(i)[k]−LMA_(i)[k]|) of each battery cell BC_(i)for each unit time. Also, the first control unit 400 may determine theaverage value of the long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|). Hereinafter, the average value is expressedas SMA_(i)[k]−LMA_(i)[k]|av. The first control unit 400 may alsodetermine the deviation for the long-term and short-term averagedifference (|SMA_(i)[k]−LMA_(i)[k]|) compared with the average value(|SMA_(i)[k]−LMA_(i)[k]|av) of the long-term and short-term averagedifference as the cell diagnosis deviation (Ddiag,i[k]). Also, the firstcontrol unit 400 may detect voltage abnormality of each battery cellBC_(i) based on the cell diagnosis deviation (Ddiag,i[k]).

In an embodiment, for at least one battery cell among the plurality ofbattery cells, the first control unit 400 may detect a battery cell thatmeets the condition in which the cell diagnosis deviation exceeds thediagnosis threshold as a voltage abnormal cell. For example, when thecell diagnosis deviation (Ddiag,i[k]) for the i^(th) battery cell BC_(i)exceeds a preset diagnosis threshold (e.g., 0.015), the first controlunit 400 may judge that voltage abnormality occurs in the correspondingi^(th) battery cell BC_(i), and detect voltage abnormality of thebattery cell.

In another embodiment, the first control unit 400 may determine astatistically variable threshold dependent on the standard deviation ofthe cell diagnosis deviations of the plurality of battery cells. Also,the first control unit 400 may filter the time series data based on thestatistically variable threshold in order to generate filtered timeseries data. Finally, the first control unit 400 may be configured todetect a voltage abnormality of the battery cell based on the time ornumber of data of the filtered time series data exceeding the diagnosisthreshold for at least one battery cell among the plurality of batterycells. Here, the feature of detecting the voltage abnormality of thebattery cell using the statistically variable threshold will bedescribed later in detail through the following embodiment considering anormalized cell diagnosis deviation for convenience of explanation.

Meanwhile, the first control unit 400 may determine a normalizationvalue corresponding to an average value of the determined long-term andshort-term average differences of the plurality of battery cells. Inaddition, the first control unit 400 may normalize the long-term andshort-term average differences according to the normalization value foreach of the plurality of battery cells.

For example, the first control unit 400 may normalize the long-term andshort-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) of each batterycell BC_(i) using a normalization criterion value in order to detectvoltage abnormality. Preferably, the normalization criterion value isthe average value (|SMA_(i)[k]−LMA_(i)[k]|av) of the long-term andshort-term average difference.

Specifically, the first control unit 400 may set the average value(|SMA_(i)[k]-LMA_(i)[k]|av) of the long-term and short-term averagedifference of the first to N^(th) battery cells BC_(i) to BC_(N) as thenormalization criterion value. The first control unit 400 may alsodivide the long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|) of each battery cell BC_(i) by thenormalization criterion value to normalize the long-term and short-termaverage difference (|SMA_(i)[k]−LMA_(i)[k]|).

Equation 6 below represents an equation normalizing the long-term andshort-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) of each batterycell BC_(i). In an embodiment, the value calculated by Equation 6 may bereferred to as a normalized cell diagnosis deviation (D*diag,i[k]).

D*diag,i[k]=(|SMA _(i) [k]−LMA _(i) [k]|)+(|SMA _(i) [k]−LMA _(i)[k]|av)  <Equation 6>

In Equation 6, 1 SMA_(i)[k]−LMA_(i)[k] is the long-term and short-termaverage difference of the i^(th) battery cell BC_(i) at the current timepoint, I SMA_(i)[k]−LMA_(i)[k]|av is the average value (normalizationcriterion value) of the long-term and short-term average differences ofall battery cells, and D*diag,i[k] is the normalized cell diagnosisdeviation of i^(th) battery cell BC_(i) at the current time point. Thesymbol ‘*’ indicates that the parameter is normalized.

The long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|) of each battery cell BC_(i) may also benormalized through the logarithmic operation of Equation 7 below. In anembodiment, the value calculated by Equation 7 may also be called anormalized cell diagnosis deviation (D*diag,i[k]).

D*diag,i[k]=Log|SMA _(i) [k]−LMA _(i) [k]|  <Equation 7>

FIG. 4 d shows the change of the normalized cell diagnosis deviation(D*diag,i[k]) of each battery cell BC_(i) according to time. The celldiagnosis deviation (D*diag,i[k]) is calculated using Equation 6. InFIG. 4 d , the horizontal axis represents time, and the vertical axisrepresents the cell diagnosis deviation (D*diag,i[k]) of each batterycell BC_(i).

Referring to FIG. 4 d , as the long-term and short-term averagedifference (|SMA_(i)[k]−LMA_(i)[k]|) of each battery cell BC_(i) isnormalized, it may be seen that the change in the long-term andshort-term average difference of each battery cell BC_(i) is amplifiedbased on the average value. Accordingly, the voltage abnormality of thebattery cell may be detected more accurately.

The first control unit 400 may determine a statistically variablethreshold dependent on the standard deviation for the normalized celldiagnosis deviations of the plurality of battery cells.

For example, the first control unit 400 may detect voltage abnormalityin each battery cell BC_(i) by comparing the normalized cell diagnosisdeviation (D*diag,i[k]) of each battery cell BC_(i) with thestatistically variable threshold (Dthreshold[k]).

For example, the first control unit 400 may determine the statisticallyvariable threshold (Dthreshold[k]) for each unit time using Equation 8below.

Dthreshold[k]=β*Sigma(D*diag,i[k])  <Equation 8>

In Equation 8, Sigma is a function that calculates the standarddeviation of the normalized cell diagnosis deviations (D*diag,i[k]) ofall battery cells BC at the time index k. In addition, β is a constantdetermined experimentally. β is a factor that determines the diagnosticsensitivity. β may be appropriately determined by trial and error sothat, when the present disclosure is applied to a cell group containinga battery cell having an actual voltage abnormality, the correspondingbattery cell may be detected as a voltage abnormal cell. In one example,β may be set to 5 or more, or 6 or more, or 7 or more, or 8 or more, or9 or more. Since Dthreshold[k] generated by Equation 8 is plural, theyconstitute time series data.

On the other hand, in the battery cell with voltage abnormality, thenormalized cell diagnosis deviation (D*diag,i[k]) is relatively largerthan that of normal battery cells. Therefore, in calculating Sigma(D*diag,i[k]) at the time index k to improve detection accuracy andreliability, it is desirable to exclude max (D*diag,i[k]) correspondingto a maximum value. Here, max is a function that returns a maximum valuefor a plurality of input variables, and the input variables arenormalized cell diagnosis deviations (D*diag,i[k]) of all battery cells.

In FIG. 4 d , the time series data representing the time change of thestatistically variable threshold (Dthreshold[k]) corresponds to aprofile indicated in the darkest color among all profiles.

The first control unit 400 may be configured to filter the normalizedlong-term and short-term average difference of each battery cell basedon the statistically variable threshold in order to generate filteredtime series data for each of the plurality of battery cells.

Specifically, the first control unit 400 may generate time series dataof the filter diagnosis values by filtering the time series data on thecell diagnosis deviation of each battery cell based on the statisticallyvariable threshold.

For example, the first control unit 400 may determine the statisticallyvariable threshold (Dthreshold[k]) at the time index k and thendetermine the filter diagnosis value (Dfilter,i[k]) by filtering thenormalized cell diagnosis deviation (D*diag,i[k]) for each battery cellBC_(i) using Equation 9 below.

Dfilter,i[k]=D*diag,i[k]−Dthreshold[k](IF D*diag,i[k]>Dthreshold[k])

Dfilter,i[k]=0(IF D*diag,i[k]<Dthreshold[k])  <Equation 9>

Two values may be assigned to the filter diagnosis value (Dfilter,i[k])for each battery cell BC_(i). That is, if the cell diagnosis deviation(D*diag,i[k]) is greater than the statistically variable threshold(Dthreshold[k]), the difference of the cell diagnosis deviation(D*diag,i[k]) and the statistically variable threshold (Dthreshold[k])is assigned to the filter diagnosis value (Dfilter,i[k]). On the otherhand, if the cell diagnosis deviation (D*diag,i[k]) is less than orequal to the statistically variable threshold (Dthreshold[k]), 0 isassigned to the filter diagnosis value (Dfilter,i[k]).

The first control unit 400 may be configured to detect a voltageabnormality of the battery cell based on the time or number of data offiltered time series data exceeding the diagnosis threshold for at leastone battery cell among the plurality of battery cells.

Specifically, the first control unit 400 may detect voltage abnormalityof the battery cell from the time that the filter diagnosis valueexceeds the diagnosis threshold or the number of data of filterdiagnosis value that exceeds the diagnosis threshold.

FIG. 4 e is a diagram showing time series data of the filter diagnosisvalue (Dfilter,i[k]) obtained by filtering the cell diagnosis deviation(D*diag,i[k]) at the time index k.

Referring to FIG. 4 e , an irregular pattern with positive values around3000 seconds is found in the filter diagnosis value (Dfilter,i[k]) of aspecific battery cell. For reference, the specific battery cell havingan irregular pattern is a battery cell having time series data indicatedby A in FIG. 4 d.

In one example, the first control unit 400 may integrate a time regionin which the filter diagnosis value (Dfilter,i[k]) is greater than thediagnosis threshold (e.g., 0) in the time series data of the filterdiagnosis value (Dfilter,i[k]) for each battery cell BC_(i), and detecta battery cell establishing the condition that the integration time isgreater than a preset criterion time as a voltage abnormal cell.

Preferably, the first control unit 400 may integrate time regions inwhich the condition that the filter diagnosis value (Dfilter,i[k]) isgreater than the diagnosis threshold is continuously satisfied. If thereare a plurality of corresponding time sections, the first control unit400 may independently calculate the integration time for each timesection.

The first control unit 400 may detect voltage abnormality of the batterycell from the time that the filter diagnosis value exceeds the diagnosisthreshold or the number of data of filter diagnosis value that exceedsthe diagnosis threshold.

For example, the first control unit 400 may integrate the number of dataincluded in the time region in which the filter diagnosis value(Dfilter,i[k]) is greater than the diagnosis threshold (e.g. 0) in thetime series data of the filter diagnosis value (Dfilter,i[k]) for eachbattery cell BC_(i), and detect a battery cell establishing thecondition that the data integration value is greater than a presetcriterion count as a voltage abnormal cell.

Preferably, the first control unit 400 may integrate only the number ofdata included in a time region in which the condition in which thefilter diagnosis value (Dfilter,i[k]) is greater than the diagnosisthreshold is continuously satisfied. If the corresponding time region isplural, the first control unit 400 may independently integrate thenumber of data of each time region.

Meanwhile, the first control unit 400 may replace V_(i)[k] of Equations1 to 5 with the normalized cell diagnosis deviation (D*diag,i[k]) ofeach battery cell BC_(i) shown in FIG. 4 d . In addition, at the timeindex k, the first control unit 400 may calculate the long-term andshort-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) of the celldiagnosis deviation (D*diag,i[k]), calculate the average value of thelong-term and short-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) ofthe cell diagnosis deviation (D*diag,i[k]), calculate the cell diagnosisdeviation (Ddiag,i[k]) corresponding to the difference of the long-termand short-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) compared tothe average value, calculate the normalized cell diagnosis deviation(D*diag,i[k]) for the long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|) using Equation 6, determine the statisticallyvariable threshold (Dthreshold[k]) for the normalized cell diagnosisdeviation (D*diag,i[k]) using Equation 8, determine the filter diagnosisvalue (Dfilter,i[k]) by filtering the cell diagnosis deviation(D*diag,i[k]) using Equation 9, and detect voltage abnormality of thebattery cell using the time series data in a recursive way.

FIG. 4 f is a graph showing the change in the long-term and short-termaverage difference (|SMA_(i)[k]−LMA_(i)[k]|) for the time series data(FIG. 4 d ) of the normalized cell diagnosis deviation (D*diag,i[k])according to time. In Equation 2, Equation 4, and Equation 5 used tocalculate the long-term and short-term average difference(|SMA_(i)[k]-LMA_(i)[k]|), V_(i)[k] may be replaced with D*diag,i[k],and Vav[k] may be replaced with the average value of D*diag,i[k].

FIG. 4 g is a graph showing the time series data of the normalized celldiagnosis deviation (D*diag,i[k]) calculated using Equation 6. In FIG. 4g , the time series data of the statistically variable threshold(Dthreshold[k]) corresponds to the profile indicated in the darkestcolor.

FIG. 4 h is a profile showing the time series data of the filterdiagnosis value (Dfilter,i[k]) obtained by filtering the time seriesdata of the cell diagnosis deviation (D*diag,i[k]) using Equation 9.

In one example, the first control unit 400 may integrate a time regionin which the filter diagnosis value (Dfilter,i[k]) is greater than thediagnosis threshold (e.g., 0) in the time series data of the filterdiagnosis value (Dfilter,i[k]) for each battery cell BC_(i), and detecta battery cell establishing the condition that the integration time isgreater than a preset criterion time as a voltage abnormal cell.

Preferably, the first control unit 400 may integrate a time region inwhich a condition that the filter diagnosis value (Dfilter,i[k]) isgreater than the diagnosis threshold is successively satisfied. If thecorresponding time region is plural, the first control unit 400 mayindependently calculate the integration time for each time region.

In another example, the first control unit 400 may integrate the numberof data included in a time region in which the filter diagnosis value(Dfilter,i[k]) is greater than the diagnosis threshold (e.g., 0) in thetime series data of the filter diagnosis value (Dfilter,i[k]) for eachbattery cell BC_(i), and detect a battery cell establishing a conditionthat the data integration value is greater than a preset criterion countas a voltage abnormal cell.

Preferably, the first control unit 400 may integrate only the number ofdata included in a time region in which a condition that the filterdiagnosis value (Dfilter,i[k]) is greater than the diagnosis thresholdis continuously satisfied. If the corresponding time region is plural,the first control unit 400 may independently integrate the number ofdata of each time region.

The first control unit 400 may additionally repeat the recursivecomputation process described above as many times as a criterion number.That is, the first control unit 400 may replace the voltage time seriesdata shown in FIG. 4 a with the time series data (e.g., the data of FIG.4 g ) of the normalized cell diagnosis deviation (D*diag,i[k]). Inaddition, at the time index k, the first control unit 400 may calculatethe long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|), calculate the average value of the long-termand short-term average difference (|SMA_(i)[k]−LMA_(i)[k]|), calculatethe cell diagnosis deviation (Ddiag,i[k]) corresponding to thedifference of the long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|) compared to the average value, calculate thenormalized cell diagnosis deviation (D*diag,i[k]) for the long-term andshort-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) using Equation6, determine the statistically variable threshold (Dthreshold[k]) forthe cell diagnosis deviation (D*diag,i[k]) using Equation 8, determinethe filter diagnosis value Dfilter,i[k] by filtering the cell diagnosisdeviation (D*diag,i[k]) using Equation 9, and detect voltage abnormalityof the battery cell using the time series data of the filter diagnosisvalue Dfilter,i[k] in a recursive way.

If the recursive computation process as described above is repeated, thevoltage abnormality of the battery cell may be diagnosed more precisely.That is, referring to FIG. 4 e , a positive profile pattern is observedonly in two time regions in the time series data of the filter diagnosisvalue (Dfilter,i[k]) of a battery cell with voltage abnormality.However, referring to FIG. 4 h , a positive profile pattern is observedin more time regions than in FIG. 4 e in the time series data of thefilter diagnosis value (Dfilter,i[k]) of a battery cell with voltageabnormality. Therefore, if the recursive computation process isrepeated, it is possible to more accurately detect the time point atwhich the voltage abnormality occurs in the battery cell.

Heretofore, a detailed method for detecting voltage abnormality by thefirst control unit 400 using the first information is described.Hereinafter, a method in which the first control unit 400 detectsbehavior abnormality using the first information will be described indetail with reference to FIGS. 5 to 9 .

FIG. 5 is a graph exemplarily showing a voltage curve C1 correspondingto a raw time series of actual voltage values of the cell voltage of thebattery cell referenced in various embodiments of the presentdisclosure, and FIG. 6 is a graph exemplarily showing a measured voltagecurve C2 obtained by synthesizing the measured noise with the raw timeseries corresponding to the voltage curve C1 of FIG. 5 . FIG. 7 is agraph exemplarily showing a first movement average curve AC1 obtained byapplying the first average filter to the voltage curve C2 of FIG. 6 ,and FIG. 8 is graph exemplarily showing a second movement average curveAC2 obtained by applying the second average filter to the voltage curveC2 of FIG. 6 . FIG. 9 is a graph exemplarily showing a voltage deviationcurve VC that is a difference between the first movement average curveAC1 of FIG. 7 and the second movement average curve AC2 of FIG. 8 .

First, referring to FIG. 5 , the voltage curve C1 is an example of a rawtime series including actual voltage values of the cell voltage of thebattery cell BC during charging over a predetermined period t1 to tM.For better understanding, the cell voltage increases linearly, and theillustration of the actual voltage values for the period before t1 andafter tM is omitted.

If the battery cell BC is normal, the cell voltage continues to risegradually during charging. On the other hand, if the battery cell BC hasbehavioral abnormality with any fault condition therein (e.g., amicro-short, a part of the electrode tab is torn), abnormal behaviorthat the cell voltage temporarily drops or rises even during chargingmay be irregularly observed.

The voltage curve C1 of FIG. 5 is related to an embodiment in which thebattery cell BC has behavior abnormality, in which the region Xrepresents a time range in which the cell voltage sharply drops asbehavior abnormality, and the region Y represents a time range in whichthe cell voltage rapidly rises as behavior abnormality. FIG. 5illustrates a cell voltage during charging, but the cell voltage of abehavior abnormal battery cell may change as behavioral abnormality evenduring discharging or resting. For example, during discharging, the cellvoltage of a normal battery cell continuously and gently falls, whilethe cell voltage of a behavior abnormal battery cell may temporarilyrise or fall sharply.

Next, referring to FIG. 6 , the voltage curve C2 represents the resultof synthesizing the measured noise with the actual cell voltage of thevoltage curve C1 of FIG. 5 . That is, the voltage curve C2 of FIG. 6represents a time series in which voltage values representing themeasured cell voltage are arranged in time order.

When M is a natural number indicating a predetermined total number ofsampling times (e.g., 300) and K is a natural number less than or equalto M, tK is the measurement timing (the K^(th) measurement timing tK) ofthe K^(th) voltage value (Vm[K]) in time order among the total M numberof voltage values included in voltage curve C2, and the time intervalbetween two adjacent measurement timings is spaced apart by apredetermined sampling time (e.g., 0.1 second). The voltage value(Vm[K]) is a data point indexed to the measurement timing tK among thetotal M number of voltage values included in the voltage curve C2.

The measured noise may be generated irregularly over time due tointernal and external factors of the voltage sensing unit 200 (e.g.,temperature of the voltage measuring device, sampling rate,electromagnetic wave, etc.). The current curve C3 is a time seriesincluding current values of the battery current measured over apredetermined period (t1 to tM). For convenience of explanation, thebattery current is exemplified as being constant during thepredetermined period (t1 to tM).

If comparing the voltage curve C2 of FIG. 6 with the voltage curve C1 ofFIG. 5 , the abnormal behavior (X, Y) can be easily identified from thevoltage curve C1 without the measured noise of FIG. 5 , while there is aproblem in that it is difficult to identify the abnormal behavior (X, Y)from the voltage curve C2 in which the measured noise is mixed over theentire predetermined period (t1 to tM).

The inventor of the present disclosure has confirmed that theabove-mentioned problem can be solved by applying the first averagefilter and the second average filter to the time series of voltagevalues (measured values) including the measured noise generated in themeasurement timing of the cell voltage. The time series of voltagevalues acquired over a predetermined period in the past for the batterycell BC to be detected can be called a ‘criterion voltage curve’, andthe time series of current values can be called a ‘criterion currentcurve’. Hereinafter, the voltage curve C2 and the current curve C3 ofFIG. 6 will be described to be assumed as the criterion voltage curve C2and the criterion current curve C3, respectively.

The first control unit 400 may be configured to determine a plurality ofsub voltage curves by applying a moving window of a first time length tothe time series of the cell voltages included in the first information.

Specifically, the first control unit 400 may determine a plurality ofsub voltage curves by applying a moving window of a first time length tothe criterion voltage curve C2. In addition, the first control unit 400may determine a plurality of sub current curves that are one-to-oneassociated with the plurality of sub voltage curves by applying themoving window of the first time length to the criterion current curveC3.

When K is a natural number less than or equal to M, the total M numberof sub voltage curves (i.e., first to M^(th) sub voltage curves) may bedetermined from the criterion voltage curve C2. The criterion voltagecurve C2 includes the total M number of voltage values (i.e., first toM^(th) voltage values) sequentially measured for every sampling time W.

The sub voltage curve SK is a subset of the criterion voltage curve C2and includes the (A/W+1) number of voltage values consecutive in timeorder. For example, when the sampling time W=0.1 seconds and the firsttime length A=10 seconds, the sub voltage curve SK is a time series of atotal of 101 voltage values, that is, from the (K−P)^(th) voltage valueto the (K+P)^(th) voltage value. P=A/2 W=50.

In FIG. 6 , RK is a sub current curve related to the sub voltage curveSK. Accordingly, the sub current curve RK may also include the (A/W+1)number of data points (current values) consecutive in time order.

As the battery current fluctuates greatly, the cell voltage alsofluctuates greatly. Abrupt fluctuations in cell voltage due to batterycurrent may be a hindrance to identifying abnormal behavior of the cellvoltage from the criterion voltage curve C2.

The first control unit 400 may determine a current change amount of thesub current curve RK, which is a difference between the maximum currentvalue and the minimum current value of the sub current curve RK. Thefirst control unit 400 may detect behavior abnormality of the batterycell from the time series of the cell voltage measured while the changein battery current is small, such as constant current charging or rest,by using a sub current curve RK.

The first control unit 400 may determine a long-term average voltagevalue and a short-term average voltage value of the sub voltage curve SKrelated to the sub current curve RK on condition that the current changeamount is less than a threshold change amount.

The first control unit 400 may execute the calculation processesdescribed below for the sub voltage curve SK under the condition thatthe current change amount of the sub current curve RK is equal to orless than the threshold change amount.

The first control unit 400 may determine a long-term average voltagevalue of each sub voltage curve SK by using the first average filter ofthe first time length.

Referring to FIG. 7 , the first average voltage curve AC1 may beobtained by applying the first average filter of the first time length Ato the criterion voltage curve C2. The first average filter is a kind oflow-pass filter, and may be a centered moving average having a subsetsize (A/W+1) corresponding to the first time length A. For example, thefirst control unit 400 determine the long-term average voltage value(Vav1[K]) indexed to the measurement timing tK by averaging the (A/W+1)number of voltage values included in the sub voltage curve SK, that is,the (K−P)^(th) voltage value to the (K−1)^(th) voltage value, the K^(th)voltage value and the (K+1)^(th) voltage value to the (K+P)^(th) voltagevalue. Equation 10 below shows the first average filter.

$\begin{matrix}{{V_{{av}1}\lbrack k\rbrack} = \frac{\sum_{i = {k - P}}^{k + P}{V_{m}\lbrack i\rbrack}}{\frac{A}{W} + 1}} & {< {{Equation}10} >}\end{matrix}$

In Equation 10, Vm[i] is the i^(th) voltage value included in thecriterion voltage curve C2, A is the first time length, W is thesampling time, P=A/2 W, and Vav1[K] is the long-term average voltagevalue at the measurement timing tK. The first control unit 400 maydetermine the first average voltage curve AC1 of FIG. 7 by substituting1 to M for K of Equation 10. The first time length A is predetermined asan integer multiple of the sampling time W. Therefore, the first timelength A indicates the size of the subset (A/W+1) used to obtain thelong-term average voltage value (Vav1[K]).

In addition, the first control unit 400 may determine a short-termaverage voltage value of the sub voltage curve by using the secondaverage filter having a second time length shorter than the first timelength.

Referring to FIG. 8 , the second average voltage curve AC2 is obtainedby applying the second average filter of the second time length Bshorter than the first time length A to the criterion voltage curve C2.The second average filter is a kind of low pass filter, and may be acentral moving average having a subset size (B/W+1) corresponding to thesecond time length B.

As an example, the first control unit 400 determine the short-termaverage voltage value (Vav2[K]) indexed to the measurement timing tK byaveraging the (B/W+1) number of voltage values included in the subvoltage curve SK, that is, the (K−Q)^(th) voltage value to the(K−1)^(th) voltage value, the K^(th) voltage value and the (K+1)^(th)voltage value to the (K+Q)^(th) voltage value. Q=B/2 W. The short-termaverage voltage value (Vav2[K]) is the average of the subset UK of thesub voltage curve SK. The subset UK is located within the time range(tK−P to tK+P) of the sub voltage curve SK and is the voltage curve of atime range (tK−Q to tK+Q) having the same measurement timing tK as thetime range (tK−P to tK+P). Equation 11 below shows the second averagefilter.

$\begin{matrix}{{V_{{av}2}\lbrack k\rbrack} = \frac{\sum_{i = {k - Q}}^{k + Q}{V_{m}\lbrack i\rbrack}}{\frac{B}{W} + 1}} & {< {{Equation}11} >}\end{matrix}$

In Equation 11, Vm[i] is the i^(th) voltage value included in thecriterion voltage curve C2, B is the second time length, W is thesampling time, Q=B/2 W, and Vav2[K] is the short-term average voltagevalue at the measurement timing tK. The first control unit 400 maydetermine the second average voltage curve AC2 of FIG. 8 by substituting1 to M for K of Equation 11 one by one. The second time length B ispredetermined as an integer multiple of the sampling time W.Accordingly, the second time length B indicates the size of the subset(B/W+1) used to obtain the short-term average voltage value (Vav2[K]).

The first time length A>the second time length B, each data point (i.e.,the long-term average voltage value) of the first average voltage curveAC1 may be called a ‘long-term average value’, and each data point(i.e., the short-term average voltage value) of the second averagevoltage curve AC2 may be called as a ‘short-term average value’. Forexample, A may be ten times greater than B.

The first control unit 400 may determine a voltage deviationcorresponding to a difference between the long-term average voltagevalue and the short-term average voltage value of the sub voltage curve.

For example, the first control unit 400 may determine a voltagedeviation associated with the sub voltage curve by subtracting the otherfrom one of the long-term average voltage value and the short-termaverage voltage value of each sub voltage curve.

Referring to FIG. 9 , the voltage deviation curve VC is a result ofsubtracting the other from one of the first average voltage curve AC1and the second average voltage curve AC2. That is, the voltage deviationcurve VC is a time series of the total M number of voltage deviationsfor a predetermined period (t1 to tM). The voltage deviation (AV[K])related to the sub voltage curve SK is a value obtained by subtractingthe other from one of the long-term average voltage value (Vav1[K]) andthe short-term average voltage value (Vav2[K]). For example,AV[K]=Vav2[K]− Vav1[K].

As described above, the long-term average voltage value (Vav1[K]) is anaverage cell voltage for a long period of the first time length Acentered on the measurement timing tK, and the short-term averagevoltage value (Vav2[K]) is an average cell voltage for a short period ofthe second time length B centered on the measurement timing tK.Therefore, by subtracting the other from one of the long-term averagevoltage value (Vav1[K]) and the short-term average voltage value(Vav2[K]) to obtain the voltage deviation (AV[K]), there is an effectthat the measured noise generated over a predetermined period before andafter the measurement timing tK is effectively removed.

There is an advantage in that the measured noise generated over acertain period before and after the measurement timing tK is offset to aconsiderable extent through the process of subtracting the other fromone of the long-term average voltage value (Vav1[K]) and the short-termaverage voltage value (Vav2[K]).

The first control unit 400 may compare each of the plurality of voltagedeviations (ΔV[K]) determined for the plurality of sub voltage curveswith at least one of a first threshold deviation TH1 and a secondthreshold deviation TH2 in order to detect behavior abnormality of thebattery cell.

For example, the first control unit 400 may compare the voltagedeviation (ΔV[K]) with the first threshold deviation TH1 and the secondthreshold deviation TH2. The first threshold deviation TH1 may be apredetermined positive number (e.g., +0.001 V), and the second thresholddeviation TH2 may be a predetermined negative number (e.g., −0.001 V)having the same absolute value as the first threshold deviation TH1.

The first control unit 400 may detect that the battery cell BC hasbehavior abnormality when among all the voltage deviations included inthe voltage deviation curve VC, a predetermined number (e.g., 10) ofvoltage deviations or more are equal to or greater than the firstthreshold deviation TH1 or equal to or less than the second thresholddeviation TH2.

The first control unit 400 may be configured to detect a behaviorabnormality in response to any two voltage deviations satisfying a firstcondition, a second condition, and a third condition, respectively,among the plurality of voltage deviations.

Specifically, the first control unit 400 may be configured to determinethat the battery cell has behavior abnormality when any two voltagedeviations among the plurality of voltage deviations determined for theplurality of sub voltage curves satisfy the first condition, the secondcondition and the third condition.

For example, the first control unit 400 may judge that the battery cellBC has behavior abnormality when among the total M number of voltagedeviations included in the voltage deviation curve VC, any two voltagedeviations satisfy the first condition, the second condition, and thethird condition. The first condition is satisfied when one of the twovoltage deviations is greater than or equal to the first thresholddeviation TH1. The second condition is satisfied when the other of thetwo voltage deviations is less than or equal to the second thresholddeviation TH2. The third condition is satisfied when the time intervalbetween the two voltage deviations is equal to or less than thethreshold time. The threshold time may be predetermined to be less thanthe first time length A. Referring to FIG. 9 , the voltage deviation(ΔV[a]) is less than or equal to the second threshold deviation TH2 (thesecond condition is satisfied), and the voltage deviation (ΔV[b]) isgreater than or equal to the first threshold deviation TH1 (the firstcondition is satisfied). Therefore, when the time interval (Δt=tb−ta)between the two voltage deviations (ΔV[a], ΔV[b]) is less than or equalto the threshold time, the battery cell BC may be detected as havingbehavior abnormality.

So far, a method in which the first control unit 400 detects at leastone abnormality among voltage abnormality and behavior abnormality byusing first information according to various embodiments of the presentdisclosure has been described with reference to FIGS. 3 to 9 .Hereinafter, an external device 2000 according to an embodiment of thepresent disclosure will be described in detail with reference to FIG. 10.

FIG. 10 is a block diagram showing a schematic configuration of theexternal device 2000 according to an embodiment of the presentdisclosure. The external device 2000 may be a dedicated device fordiagnosing a battery cell. The external device 2000 may include astorage unit 2100 and a third control unit 2200.

The storage unit 2100 may collect the charging and discharging dataincluded in the first information and store the collected charging anddischarging data.

The storage unit 2100 is not particularly limited in its type as long asit can record and erase data and/or information. As an example, thestorage unit 2100 may be a RAM, a ROM, a register, a flash memory, ahard disk, or a magnetic recording medium.

The storage unit 2100 may be electrically connected to the third controlunit 2200 through a data bus or the like so that it can be accessed bythe third control unit 2200.

The storage unit 2100 stores and/or updates and/or erase and/or transmita program including various control logic performed by the third controlunit 2200, and/or data generated when the control logic is executed,and/or preset data, parameters, lookup information/table, etc.

First, an embodiment in which the diagnostic information of the batterycell included in the second information is information about lithiumprecipitation diagnosis of the battery cell will be described.

In an embodiment, the second information may represent whether theaccumulated capacity difference change amount is greater than or equalto a threshold value. Here, the accumulated capacity difference changeamount may be the sum of capacity difference change amounts. That is,the sum of a plurality of capacity difference change amounts may becalculated as the accumulated capacity difference change amount. Here,each of the capacity difference change amounts is a difference between acapacity difference of a k^(th) charging and discharging cycle of thebattery cell and a capacity difference of a k−1^(th) charging anddischarging cycle of the battery cell (k is a natural number greaterthan or equal to 2). Here, the capacity difference of each charging anddischarging cycle corresponds to a difference between a chargingcapacity of the battery cell during the charging process of the chargingand discharging cycle and a discharging capacity of the battery cellduring the discharging process of the charging and discharging cycle.Here, each of the charging capacity and the discharging capacity may bederived from data obtained from the current measuring unit and includedin the first information.

The third control unit 2200 may generate second information includingthe diagnostic information of the battery cell related to a lithiumprecipitation diagnosis by using the first information. The number ofcharging and discharging cycles required to generate the secondinformation may be set in advance. In one example, the number ofcharging and discharging cycles required to generate the secondinformation may be 20.

For example, the charging and discharging cycle may include a chargingcycle and a discharging cycle. The charging cycle may mean charging abattery from the lower limit to the upper limit of a preset chargingvoltage region and stopping the charging while maintaining the batterycell temperature constant. The discharging cycle may mean stabilizingthe battery for a predetermined time after the charging cycle iscompleted, then discharging the battery from the upper limit to thelower limit of a preset discharging voltage region and stopping thedischarging while maintaining the battery cell temperature to be thesame as the charging cycle. The charging voltage region and thedischarging voltage region may be the same or different. However, inperforming a plurality of charging and discharging cycles, it ispreferable that the charging voltage regions of the charging cycles arethe same and the discharging voltage regions of the discharging cyclesare also the same.

In another example, the charging cycle means charging the battery fromthe lower limit to the upper limit of the preset charging voltage regionand stopping charging while keeping the battery cell temperatureconstant. The discharging cycle starts discharging from the upper limitof the preset discharging voltage region and stops the discharging whenthe accumulated current value reaches a preset discharging capacity byintegrating the discharging current. In performing the plurality ofcharging and discharging cycles, it is preferable that the chargingvoltage regions of the charging cycles are the same and the dischargingcapacity of the discharging cycles are the same.

The third control unit 2200 may derive a charging capacity and adischarging capacity of the battery cell from the data included in thefirst information. Specifically, the third control unit 2200 may receivethe current measurement value included in the first information in theK^(th) (k is a natural number greater than or equal to 2) charging anddischarging cycle by using the information about the charging current ordischarging current included in the first information, and calculate acharging capacity (ChgAh[k]) and a discharging capacity (DchgAh[k]).

For example, the third control unit 2200 may initialize the charging anddischarging cycle index k to 1, and initialize the first capacitydifference change amount ΔdAh[1] and the first accumulated capacitydifference change amount (Σ_(i=1) ¹ ΔdAh) to 0, respectively.

The third control unit 2200 may start the first charging and dischargingcycle for the battery. In this specification, when the external device2000 starts the charging and discharging cycle, it may mean that datacorresponding to the charging and discharging cycle is obtained usingthe first information.

The third control unit 2200 may calculate the charging capacity(ChgAh[1]) and the discharging capacity (DchgAh[I]) by using the currentmeasurement value included in the first information during the firstcharging and discharging cycle.

The first information may include information about the charging cycleperformed in the preset charging voltage region and the dischargingcycle performed in the preset discharging voltage region.

The charging voltage region and the discharging voltage region may bethe same or different. Preferably, the discharging cycle is initiatedafter the voltage of the battery cell is stabilized after the chargingcycle is finished. Also, the discharging cycle may be ended when thevoltage of the battery cell reaches a preset discharging end voltage orwhen the integrated value of the discharging current reaches a presetdischarging capacity. When the start and end of the charging cycle andthe discharging cycle are controlled based on the voltage value, theexternal device 2000 may refer to the voltage measurement value includedin the first information. The voltage measurement value included in thefirst information may be a value measured through the voltage sensingunit 200.

The third control unit 2200 may continue the charging and dischargingcycle until the index k for the charging and discharging cycle is equalto n. n is a preset natural number that is the total number of chargingand discharging cycles that can proceed to detect lithium precipitationabnormality. In one example, n may be 20.

The third control unit 2200 may determine the capacity difference(dAh[k]) corresponding to the difference between the charging capacity(ChgAh[k]) and the discharging capacity (DchgAh[k]). That is, thecapacity difference (dAh[k]) may be calculated as a difference betweenthe charging capacity (ChgAh[k]) and the discharging capacity(DchgAh[k]).

For example, the first charging and discharging cycle (e.g., k=1) willbe described. The third control unit 2200 may record the determinedcapacity difference (dAh[1]) together with a time stamp in the storageunit 2100. In one example, the capacity difference (dAh[I]) may bedetermined by subtracting the discharging capacity (DchgAh[1]) from thecharging capacity (ChgAh[1]). The third control unit 2200 may determinethe capacity difference (dAh[1]) corresponding to the difference betweenthe charging capacity (ChgAh[1]) and the discharging capacity(DchgAh[1]), and record the determined capacity difference (dAh[1]) inthe storage unit 2100 together with a time stamp. In one example, thecapacity difference (dAh[I]) may be determined by subtracting thedischarging capacity (DchgAh[1]) from the charging capacity (ChgAh[1]).

The third control unit 2200 may determine the K^(th) capacity differencechange amount (ΔdAh[k]) by subtracting the capacity difference (dAh[k])of the K^(th) charging and discharging cycle from the capacitydifference (dAh[k−1]) of the k−1^(th) charging and discharging cycle.Namely, the capacity difference change amount (ΔdAh[k]) may becalculated as a difference between the capacity difference (dAh[k]) ofthe K^(th) charging and discharging cycle and the capacity difference(dAh[k−1]) of the k−1^(th) charging and discharging cycle.

For example, the third control unit 2200 may determine the secondcapacity difference change amount (ΔdAh[2]) by subtracting the capacitydifference (dAh[2]) of the second charging and discharging cycle fromthe capacity difference (dAh[1]) of the first charging and dischargingcycle.

The third control unit 2200 may update the accumulated capacitydifference change amount by adding the K^(th) capacity difference changeamount (ΔdAh[k]) to the accumulated capacity difference change amount ifthe K^(th) capacity difference change amount (ΔdAh[k]) is greater thanthe criterion value. Here, the accumulated capacity difference changeamount may be the sum of the plurality of calculated capacity differencechange amounts.

For example, the third control unit 2200 updates the accumulatedcapacity difference change amount by adding the second capacitydifference change amount (ΔdAh[2]) to the first accumulated capacitydifference change amount (Σ_(i=1) ¹ ΔdAh), and determines the updatedvalue to the second accumulated capacity difference change amount(Σ_(i=1) ² ΔdAh). For reference, the first accumulated capacitydifference change amount (Σ_(i=1) ¹ ΔdAh) is 0 that is an initializationvalue.

If the updated accumulated capacity difference change amount is greaterthan or equal to a threshold value, the third control unit 2200 maydetect that there is lithium precipitation abnormality and generate thesecond information.

The threshold value may mean a value suitable for detecting lithiumprecipitation abnormality. For example, the threshold value may be 0.1%of the battery capacity. The threshold value may be a value preset inthe external device 2000 or a value included in the first information.

If the updated accumulated capacity difference change amount is greaterthan or equal to the threshold value, the third control unit 2200 mayjudge that lithium precipitation abnormality occurs inside the battery,and generate the second information. That is, the second information mayrepresent whether the accumulated capacity difference change amount isgreater than or equal to the threshold value.

In another embodiment, the second information may also represent acapacity difference change amount between successive charging anddischarging cycles of the battery cell. Here, the capacity differencefor each charging and discharging cycle of the battery cell may be adifference between (i) the charging capacity of the battery cell duringthe charging process of the charging and discharging cycle of thebattery cell and (ii) the discharging capacity of the battery cellduring the discharging process of the charging and discharging cycle ofthe battery cell. In this case, the first control unit 400 may calculatethe accumulated capacity difference change amount by calculating the sumof the capacity difference change amounts from the second information.Also, the first control unit 400 may judge whether the accumulatedcapacity difference change amount is greater than or equal to thethreshold value.

Next, an embodiment in which the diagnostic information of the batterycell included in the second information is information about a parallelconnection abnormality of the battery cell will be described.

The second information may represent a parallel connection abnormalityof a plurality of unit cells included in the battery cell based on aresult of monitoring a change over time of the estimated capacity valueby the external device 2000. Here, the estimated capacity value mayrepresent a full charging capacity of the battery cell based on chargingand discharging data. Here, the charging and discharging data mayinclude a voltage time series representing the change over time of thevoltage of the battery cell and a current time series representing thechange over time of the charging and discharging current of the batterycell.

The storage unit 2100 may collect charging and discharging dataincluding the voltage time series representing the change over time ofthe voltage across both ends of the battery cell and the current timeseries representing the change over time of the charging and dischargingcurrent flowing through the battery cell by using the information on thecharging current or the discharging current included in the firstinformation, and store the collected charging and discharging data.

The third control unit 2200 may determine an estimated capacity valuerepresenting the full charging capacity of the battery cell based on thecharging and discharging data. Also, the third control unit 2200 maydetect a parallel connection abnormality by monitoring the change overtime of the determined estimated capacity value. The diagnosticinformation of the battery cell corresponding to the parallel connectionabnormality may be included in the second information.

A method for the external device 2000 to detect a parallel connectionabnormality will be described in detail with reference to FIGS. 11 to 14.

FIG. 11 is a diagram exemplarily showing a schematic configuration ofthe battery cell shown in FIG. 3 , FIG. 12 is a diagram referred to forillustrating a first capacity abnormality (incomplete disconnectionfailure) of a battery cell, and FIG. 13 is a diagram referred to forillustrating a second capacity abnormality (complete disconnectionfailure) of a battery cell.

Referring to FIG. 11 , the battery B includes an electrode assemblyB200, a positive electrode lead B210, a negative electrode lead B220,and an exterior B230.

The electrode assembly B200 is an example of parallel connection of aplurality of unit cells BUC1 to UCM (M is a natural number of 2 ormore). The unit cell BUC includes a separator B203, a positive electrodeplate B201, and a negative electrode plate B202 insulated from thepositive electrode plate B201 by a separator B203.

The positive electrode plate B201 has a positive electrode tab B205 thatis a portion connected to one end of the positive electrode lead B210,and the negative electrode plate B202 has a negative electrode tab B206that is a portion connected to one end of the negative electrode leadB220.

In a state where the positive electrode tabs B205 and the negativeelectrode tabs B206 of the plurality of unit cells UC1 to UCM arecoupled to one end of the positive electrode leads B210 and the negativeelectrode leads B220, respectively, the electrode assembly B200 isaccommodated in the exterior B230 together with an electrolyte. Theother ends of the positive electrode lead B210 and negative electrodelead B220 exposed to the outside of the exterior B230 are provided as apositive electrode terminal and a negative electrode terminal of thebattery B, respectively.

Referring to FIG. 12 , the first capacity abnormality of the electrodeassembly B200 may mean a state in which the contact resistances R1, R2between the unit cells UC1, UC2 and the electrode leads B210, B220 varywidely and irregularly since a minor damage and/or incompletedisconnection failure occurs in the electrode tabs B205, B206 of someunit cells UC1, UC2 among the plurality of unit cells UC1 to UCM.

The incomplete disconnection failure may mean a state in whichdisconnected parts of the electrode tabs B205, B206 do not maintain astate of being separated from each other, the disconnected parts areflexibly connected and separated according to the contraction-expansionof the battery B, and the contact area during connection is alsovariable.

While the contact resistance in the unit cells UC1, UC2 is kept small,all unit cells UC1 to UCM are charged and discharged almost equally, andas the contact resistances R1, R2 increase, the unit cells UC1, UC2closely come into a state of being separated (disconnected) from theremaining unit cells UC3 to UCM, and as a result, the capacity of thebattery B shows an irregular behavior of rapidly increasing ordecreasing depending greatly on the contact resistances R1, R2 of theunit cells UC1, UC2. For example, while a large tensile force actsbetween the electrode tabs B205, B206 of the unit cells UC1, UC2 and theelectrode leads B210, B220 due to swelling of the battery B, the contactresistances R1, R2 of the unit cells UC1, UC2 increase, and conversely,as the tensile force gradually decreases, the contact resistances R1, R2of the unit cells UC1, UC2 decrease.

Referring to FIG. 13 , the second capacity abnormality of the electrodeassembly B200 is equalized to a state in which some unit cells UC1, UC2among the plurality of unit cells UC1 to UCM are irreversibly broken,namely in which the charging and discharging current path between theunit cells UC1, UC2 and the electrode leads B210, B220 is irreversiblylost due to a complete disconnection failure of the unit cells UC1, UC2.

The complete disconnection failure is a state in which the electrodetabs B205, B206 or the electrode plates B201, B202 of the unit cellsUC1, UC2 are cut into multiple parts that are spaced apart so as not tobe reconnected, and is distinguished from the aforementioned incompletedisconnection failure.

The occurrence of a second capacity abnormality by the unit cells UC1,UC2 at a certain time during manufacture or use of the battery B maymean that the unit cells UC1, UC2 are irretrievably removed from theelectrode leads B210, B220. Thus, from the specific time when the secondcapacity abnormality occurs, the unit cells UC1, UC2 do not contributeto the charging and discharging of the battery B at all, so the capacityof the battery B depends only on the capacity of the remaining unitcells UC3 to UCM excluding the unit cells UC1, UC2.

The external device 2000 may repeat periodically or aperiodically aprocedure for determining an estimated capacity value representing thefull charging capacity (FCC) of the battery cell by applying a capacityestimation model to the charging and discharging data. The externaldevice 2000 may monitor a change over time of the estimated capacityvalue. The capacity estimation model is a kind of algorithm thatreceives charging and discharging data and provides an estimatedcapacity value as a corresponding output, and is a combination ofseveral functions.

Specifically, the capacity estimation model may include (i) a firstfunction that calculates a current integration value of the charging anddischarging current of the battery B over a certain period or variableperiod in the past from the current time series of the battery B, (ii) asecond function that calculates an OCV (Open Circuit Voltage) of thebattery B over a certain period or variable period in the past from thevoltage time series and/or the current time series of the battery B,(iii) a third function that calculates a SOC (State Of Charge) of thebattery B from the OCV of the battery B using a given OCV-SOCrelationship table, and (iv) a fourth function that calculates anestimation result of the full charging capacity of the battery B, namelyan estimated capacity value, from the ratio of the current integrationvalue and the SOC change value respectively calculated for a commonperiod. Equation 12 below is an example of the fourth function.

$\begin{matrix}{{FCC}_{t2} = \frac{\Delta{Ah}_{{t1} - {t2}}}{\Delta{SOC}_{{t1} - {t2}}}} & {< {{Equation}12} >}\end{matrix}$

In the above equation, ΔAh_(t1-t2) is a current integration value of thecharging and discharging current repeatedly measured over a time rangebetween two times t1 and t2, ASOC_(t1-t2) is a SOC change value over thetime range between two times t1 and t2, and FCC_(t2) is an estimatedcapacity value representing the full charging capacity at time t2. Timet1 may be a recent time that precedes time t2 and satisfies that theabsolute value of ΔAh_(t1-t2) is greater than or equal to a criterionintegration value and/or that the absolute value of ΔSOC_(t1-t2) isgreater than or equal to a criterion change value. The criterionintegration value and the criterion change value may be predetermined inorder to prevent deterioration in accuracy of FCC_(t2) due to a verysmall absolute value of ΔAh_(t1-t2) and/or ΔSOC_(t1-t2).

In calculating the current integration value, the charging current maybe set as a positive number and the discharging current may be set as anegative number. Time t2 is a calculation timing of the full chargingcapacity. If the full charging capacity is repeatedly calculated atevery first time interval, it will be easily understood by those skilledin the art that time t2 is shifted to the first time interval.

For example, when the current integration value and the SOC change valueover a common period in the past are +100 Ah [ampere-hour] and +80%,respectively, the estimated capacity value of the full charging capacityis 125 Ah. As another example, if the current integration value and theSOC change value over a common period in the past are −75 Ah[ampere-hour] and −60%, respectively, the estimated capacity value ofthe full charging capacity is also 125 Ah.

The full charging capacity represents a maximum storage capacity of thebattery B, namely a remaining capacity of the battery B at SOC 100%. Itis normal that the full charging capacity slowly decreases as thebattery B degrades. Therefore, when the amount of reduction in the fullcharging capacity for a short time interval exceeds a certain level, itrepresents that a first capacity abnormality or a second capacityabnormality occurs.

FIG. 14 is an exemplary diagram referred to for illustrating arelationship between the capacity abnormality and the full chargingcapacity of the battery cell.

Referring to FIG. 14 , the curve C21 illustrates a change over time ofthe full charging capacity of a normal battery cell. For betterunderstanding, the curve C21 is simplified to show that the fullcharging capacity of a normal battery cell decreases linearly over time.

The curve C22 exemplifies a change over time of the full chargingcapacity of the battery B when the first capacity abnormality and thesecond capacity abnormality occur sequentially. As shown in FIG. 12 ,the curve C22 represents the full charging capacity of the battery B inwhich a first capacity abnormality occurs due to a minute damage and/orincomplete disconnection failure of the unit cells UC1, UC2. In thecurve C22, the full charging capacity gradually decreases from time ta(e.g., release time of the battery cell) to time tb, then decreasesrapidly from time tb to time tc, and then increases rapidly from time tcto time td. That is, the decrease amount of the full charging capacitybetween time tb and time tc is mostly recovered between time tc and timetd. As described above with reference to FIG. 12 , this is because thecontact resistances R1, R2 of the unit cells UC1, UC2 increase rapidlybetween time tb and time tc and then return to normal levels betweentime tc and time td.

This means that if the first capacity abnormality lasts for a long term,it may develop (intensify) into the second capacity abnormality. Seeingthe curve C22, after time td, from time te to time tf, similar to theprevious period from time tb to time tc, the full charging capacityrapidly decreases. However, in contrast to the behavior at times tc totd, even after time tf when the rapid decrease of the full chargingcapacity stops, the curve C22 has a similar slope to the curve C21 in astate where the full charging capacity does not recover to a normallevel. As described above with reference to FIG. 13 , this is becausethe complete disconnection failure of the unit cells UC1, UC2, namelythe second capacity abnormality, occurs near time te.

The external device 2000 judges whether the first capacity abnormalityand/or the second capacity abnormality of the parallel connection B200occurs by monitoring the full charging capacity according to the curveC22, namely the change over time (time series) of the estimated capacityvalue.

Specifically, the external device 2000 judges whether the first capacityabnormality and/or the second capacity abnormality of the parallelconnection B200 occurs by applying a diagnostic logic to two estimatedcapacity values at the first time and the second time, which are shiftedto the first time interval with a second time interval equal to orgreater than the first time interval. The second time is a time behindthe first time by the second time interval, and the first time and thesecond time may be set by the external device 2000 to increase by thefirst time interval for each first time interval. The first timeinterval may be equal to the collection period of the charging anddischarging data (or the calculation period of the estimated capacityvalue), and the second time interval may be an integer multiple (e.g.,10 times) of the first time interval.

The diagnostic logic may include (i) a first routine that determines athreshold capacity value for the second time to be smaller than theestimated capacity value at the first time, and (ii) a second routinethat compares the estimated capacity value at the second time with thethreshold capacity value for the second time.

In the first routine, the threshold capacity value for the second timemay be equal to the result of subtracting a criterion capacity valuefrom the estimated capacity value at the first time or the result ofmultiplying the estimated capacity value at the first time by acriterion factor less than 1. The criterion capacity value may berecorded in a memory 120 as a predetermined value in consideration ofthe total number M of the plurality of unit cells UC1 to UCM included inthe battery B and the design capacity of the battery B (full chargingcapacity in a new state). The criterion factor may be recorded in amemory 120 as a predetermined value (e.g., (M−1)/M, (M−2)/M) inconsideration of the total number M of the plurality of unit cells UC1to UCM included in the battery B. The curve C23 of FIG. 14 illustratesthe change over time of the threshold capacity value calculated byapplying the first routine to the curve C22.

If the estimated capacity value at the second time is less than thethreshold capacity value for the second time, the external device 2000may judge that at least one of the first capacity abnormality and thesecond capacity abnormality has occurred in the parallel connectionB200. In addition, the external device 2000 may generate secondinformation representing whether or not there is a parallel connectionabnormality.

Specifically, the external device 2000 may increase a diagnosis count by1 whenever the estimated capacity value at the second time is less thanthe threshold capacity value for the second time. The calculationcircuit 130 may reset the diagnosis count to an initial value (e.g., 0)or decrease the diagnosis count by 1 whenever the estimated capacityvalue at the second time is greater than or equal to the thresholdcapacity value for the second time. In response to that the estimatedcapacity value at the second time before the diagnosis count reaches thethreshold count is recovered to the threshold capacity value for thesecond time or higher, the external device 2000 may judge that theabnormality type of the parallel connection B200 is the first capacityabnormality. In response to that the diagnosis count reaches thethreshold count (e.g., 5), the external device 2000 may judge that thesecond capacity abnormality of the parallel connection B200 hasoccurred.

In FIG. 14 , times ta+, tb+, tc+, td+, te+, and tf+ are times shifted inthe positive direction by the second time interval from times ta, tb,tc, td, te, and tf, respectively. Over the time range between time txand time ty, the curve C22 lies below the curve C22. Therefore, fromtime tx to time ty, the diagnosis count increases by 1 for each firsttime interval. The external device 2000 may activate a predeterminedprotection function related to the second capacity abnormality of thebattery B in response to the diagnosis count reaching the thresholdcount before time ty.

Finally, an embodiment in which the diagnostic information of thebattery cell included in the second information is information about theinternal short circuit of the battery cell will be described.

The second information may represent whether the battery cell has aninternal short circuit based on a first SOC change and a criterionfactor of the battery cell. Here, the criterion factor may be determinedby applying a statistical algorithm to the first SOC change of at leasttwo battery cells among the plurality of battery cells. Here, the firstSOC change may be a difference between a first SOC at a first chargingtime point of each battery cell and a second SOC at a second chargingtime point. Here, the first SOC may be estimated by applying a SOCestimation algorithm to a state parameter of the battery cell at thefirst charging time point. Here, the second SOC may be estimated byapplying the SOC estimation algorithm to the state parameter of thebattery cell at the second charging time point. Here, the stateparameter may be obtained based on the first information.

The storage unit 2100 may obtain a state parameter of each of theplurality of battery cells by using information included in the firstinformation. The state parameter may include voltage, current, and/ortemperature of the battery cell BC.

The third control unit 2200 may monitor a SOC change of the battery cellBC during a charging period, a discharging period, and/or an idle periodof the battery pack 10 by applying a SOC estimation algorithm to thestate parameter of the battery cell BC. For example, as the SOCestimation algorithm, an OCV-SOC relationship map or a Kalman filter maybe used. The OCV-SOC relationship map and the Kalman filter are widelyused techniques for SOC estimation, and thus will not be described indetail.

The third control unit 2200 may determine the first SOC change, which isa difference between the first SOC at the first charging time point andthe second SOC at the second charging time point, for each battery cellBC by applying the SOC estimation algorithm to the state parameters ofeach of the plurality of battery cells BC₁ to BC_(N) obtained duringcharging. The first charging time point and the second charging timepoint are not particularly limited as long as they are two differenttime points within the latest charging period.

The third control unit 2200 may determine a criterion factor by applyinga statistical algorithm to the first SOC changes of at least two batterycells among the plurality of battery cells. The criterion factor may bea representative value for the first SOC changes of at least two batterycells among the plurality of battery cells BC₁ to BC_(N). For example,the criterion factor may be an average value or a median value of thefirst SOC changes of at least two battery cells among the plurality ofbattery cells.

The third control unit 2200 may detect an internal short circuitabnormality of each battery cell based on the first SOC change of eachbattery cell and the criterion factor. Also, the diagnostic informationof the battery cell corresponding to whether or not the battery cell hasan internal short circuit may be included in the second information.

Hereinafter, a method of detecting an internal short circuit abnormalityby the external device 2000 will be described in detail with referenceto FIGS. 15 to 18 .

FIG. 15 is a diagram referred to for explaining an exemplary equivalentcircuit of a battery cell. In this specification, a normal battery cellrefers to a battery cell having no internal short circuit abnormalityamong the plurality of battery cells BC₁ to BC_(N), and an abnormalbattery cell refers to a battery cell having an internal short circuitabnormality among the plurality of battery cells BC₁ to BC_(N).

Referring to FIG. 15 , a normal battery cell may be equivalent as aseries circuit of a DC voltage source (V_(DC)), an internal resistancecomponent (R₀), and an RC pair (R₁, C). In comparison, an abnormalbattery cell may be equivalent to a battery cell in which an additionalresistance component (R_(ISC)) is connected between both ends of aseries circuit corresponding to a normal battery cell. The additionalresistance component (R_(ISC)) acts as a path for a leakage current(I_(ISC)).

When an abnormal battery cell is charged, some of the charging power isconsumed as a leakage current (I_(ISC)) without being stored in theabnormal battery cell. Also, when an abnormal battery cell isdischarged, some of the discharging power is consumed as a leakagecurrent (I_(ISC)) without being supplied to an electric load. Forreference, when the abnormal battery cell is idle, the energy stored inthe abnormal battery cell is consumed as a leakage current (I_(ISC)),similar to discharging. The decrease in the resistance value of theresistor (R_(ISC)) means an increase in internal short circuitabnormality, and as the internal short circuit abnormality worsens, theamount of power consumed as a leakage current (I_(ISC)) may increase.

As a result, during charging, the voltage change (i.e., the increaseamount of SOC) of the abnormal battery cell is smaller than that of thenormal battery. Meanwhile, during discharging, the voltage change (i.e.,the decrease amount of SOC) of the abnormal battery cell is greater thanthat of the normal battery cell.

FIGS. 16 to 18 are exemplary graphs referred to for comparing SOCchanges of the battery cell according to the presence or absence of aninternal short circuit abnormality. FIGS. 16 to 18 respectivelyillustrate the change of the charging and discharging current, thevoltage of the battery cell BC, and the SOC of the battery cell BC inthe same period.

Referring to FIG. 16 , the time point t0 and the time point t4 representtime points at which an idle state is switched to a charging state, thetime point t1 and the time point t5 represent time points at which acharging state is switched to an idle state, the time point t2represents a time point at which an idle state is switched to adischarging state, and the time point t3 represents a time point atwhich a discharging state is switched to an idle state. That is, in FIG.16 , the period from the time point t0 to the time point t1 and theperiod from the time point t4 to the time point t5 are a chargingperiod, the period from the time point t2 to the time point t3 is adischarging period, and the remaining period is a rest period. Forconvenience of explanation, in FIG. 16 , a positive value is assigned tothe charging current of each charging period, a negative value isassigned to the discharging current of the discharging period, and thecurrent in each period is illustrated as being constant.

In FIG. 17 , a curve VC2 represents a voltage curve of a normal batterycell corresponding to the current curve shown in FIG. 16 , and a curveVC3 represents a voltage curve of an abnormal battery cell correspondingto the current curve shown in FIG. 16 . The curve VC2 may be treated asa time series of an average voltage of the plurality of battery cellsBC₁ to BC_(N). The external device 2000 may periodically oraperiodically obtain a state parameter of each of the plurality ofbattery cells BC₁ to BC_(N) and record a time series of the stateparameter in the storage unit 2100.

Referring to FIG. 17 , in the charging period, the voltages of both thenormal battery cell and the abnormal battery cell gradually increase.However, since the abnormal battery cell has a lower charging powercapacity than the normal battery cell, the voltage increase of theabnormal battery cell is smaller than that of the normal battery cell.

In the discharging period, the voltages of both the normal battery celland the abnormal battery cell gradually decreases. However, in theabnormal battery cell, an additional power is consumed due to theleakage current (I_(ISC)) in addition to the discharging power of thenormal battery cell, so the voltage decrease amount of the abnormalbattery cell is greater than that of the normal battery cell.

In FIG. 18 , the curve VC4 represents a SOC curve of a normal batterycell corresponding to the voltage curve VC2 shown in FIG. 17 , and thecurve VC5 represents a SOC curve of an abnormal battery cellcorresponding to the voltage curve VC3 shown in FIG. 17 . The curve VC4may also be treated as a time series of an average SOC of the pluralityof battery cells BC₁ to BC_(N).

The external device 2000 may monitor a SOC change of the battery cell BCduring a charging period, a discharging period, and/or an idle period ofthe battery pack 10 by applying a SOC estimation algorithm to the stateparameter of the battery cell BC. For example, as the SOC estimationalgorithm, an OCV-SOC relationship map or a Kalman filter may be used.The OCV-SOC relationship map and the Kalman filter are widely usedtechniques for SOC estimation, and thus will not be described in detail.

Referring to FIG. 18 , in the charging period, the abnormal battery cellhas a smaller SOC increase rate and amount than the normal battery cell.In the discharging period, the abnormal battery cell has a higher SOCdrop rate and amount than the normal battery cell. In addition, in theidle period, the SOC of the normal battery cell is generally constant,while the SOC of the abnormal battery cell is gradually decreasing eventhough the charging and discharging current does not flow.

The external device 2000 may execute a diagnosis process for detectingan internal short circuit abnormality of the battery cell BC based onthe SOC changes of all of the plurality of battery cells BC₁ to BC_(N)in a recent charging period, whenever the battery pack 10 is charged.For example, when the external device 2000 switches from charging toidle at the time point t1, the internal short circuit abnormality of thebattery cell BC may be detected based on the SOC changes of all of theplurality of battery cells BC₁ to BC_(N) obtained during the chargingperiod (t0 to t1).

As another example, when the external device 2000 switches from chargingto idle at time point t5, an internal short circuit abnormality may bedetected based on the SOC changes of all of the plurality of batterycells BC₁ to BC_(N) obtained in the recent charging period (t4 to t5).

Alternatively, whenever the battery pack 10 is charged or discharged,the external device 2000 may execute a diagnostic process for detectingan internal short circuit abnormality of the battery cell BC based onthe SOC changes of all of the plurality of battery cells BC₁ to BC_(N)in the recent charging period and the SOC changes of all of theplurality of battery cells BC₁ to BC_(N) in the recent dischargingperiod.

For example, when the external device 2000 switches from the dischargingstate to the idle state at time point t3, the external device 2000 maydetect an internal short circuit abnormality of the battery cell BCbased on the SOC changes of all of the plurality of battery cells BC₁ toBC_(N) obtained in the recent charging period (t0 to t1) and on the SOCchanges of all of the plurality of battery cells BC₁ to BC_(N) obtainedin the recent discharging period (t2 to t3).

As another example, when switching from the charging state to the idlestate at time point t5, the external device 2000 may detect an internalshort circuit abnormality of the battery cell BC based on the SOCchanges of all of the plurality of battery cells BC₁ to BC_(N) obtainedin the recent discharging period (t2 to t3) and the SOC changes of allof the plurality of battery cells BC₁ to BC_(N) obtained in the recentcharging period (t4 to t5).

In FIGS. 16 to 18 , the idle mode is located between the charging periodand the discharging period, but this is only one example. For example,it is possible to switch from a charging state to a discharging statewithout an idle state, or from a discharging state to a charging statewithout an idle state.

Hereinafter, a battery cell abnormal state diagnosis method of thepresent disclosure using the battery cell diagnosing apparatus 1000 andthe external device 2000 will be described in detail. The operation ofthe control circuit 220 will be described in more detail in variousembodiment(s) of the battery diagnosing method.

In this specification, what the external device 2000 performs, it mayinclude that the third control unit 2200 performs.

Hereinafter, a battery cell diagnosing method abnormal state using thebattery cell diagnosing apparatus 1000 and the external device 2000 ofthe present disclosure described above will be described in detail. Theoperation of the control circuit 220 will be described in more detail invarious embodiment(s) of the battery diagnosis method.

FIG. 19 is a flowchart in which the battery cell diagnosing apparatus1000 according to an embodiment of the present disclosure diagnoses anabnormal state of a battery cell using the external device 2000.

In the step S1000, the battery cell diagnosing apparatus 1000 may obtaindata. For example, the battery cell diagnosing apparatus 1000 may obtaindata measured by the current measuring unit 100 or the voltage sensingunit 200 using the data obtaining unit 300. Specifically, the batterycell diagnosing apparatus 1000 may obtain data on at least one of thecharging current, discharging current, and voltage signal measured bythe current measuring unit 100 or the voltage sensing unit 200 by usingthe data obtaining unit 300. In addition, the battery cell diagnosingapparatus 1000 may obtain data on the temperature signal detected by thetemperature sensor T using the data obtaining unit 300.

In the step S2000, the battery cell diagnosing apparatus 1000 maygenerate first information. For example, the battery cell diagnosingapparatus 1000 may generate the first information of the battery cellbased on the data obtained in the step S1000. The first information mayinclude data about at least one of a charging current, a dischargingcurrent, and a voltage signal of a battery cell that is a target ofabnormality judgment.

In the step S3000, the battery cell diagnosing apparatus 1000 maytransmit the generated first information to the external device 2000.

In the step S4000, the battery cell diagnosing apparatus 1000 may detectvoltage abnormality. This will be described in detail with reference toFIGS. 20 to 24 .

In the step S5000, the battery cell diagnosing apparatus 1000 may detectbehavior abnormality. This will be described in detail with reference toFIGS. 25 to 26 .

In the step S7000, the external device 2000 may detect lithiumprecipitation abnormality. This will be described in detail withreference to FIGS. 27 to 32 .

In the step S8000, the external device 2000 may generate secondinformation. The second information may include whether lithiumprecipitation abnormality is detected.

In the step S9000, the external device 2000 may transmit the secondinformation to the battery cell diagnosing apparatus 1000.

In the step S6000, the battery cell diagnosing apparatus 1000 maydiagnose a battery cell abnormal state. The battery cell diagnosingapparatus 1000 may diagnose an abnormal state of the battery cell basedon the voltage abnormality, the behavior abnormality, and the secondinformation.

FIGS. 20 to 24 are flowcharts illustrating in detail a process ofdetecting voltage abnormality by the battery cell diagnosing apparatus1000 according to an embodiment of the present disclosure.

FIG. 20 is a flowchart exemplarily showing a method for detectingvoltage abnormality according to an embodiment of the presentdisclosure. The method of FIG. 20 may be periodically executed everyunit time by the first control unit 400.

In the step S4310, the first control unit 400 may collect a voltagesignal representing the cell voltage of each of the plurality of batterycells BC₁ to BC_(N) included in the first information, and generate timeseries data of the cell voltage of each battery cell BC (see FIG. 4 a ).In the time series data of the cell voltage, the number of data mayincrease by 1 every time unit time elapses.

For example, V_(i)[k] or VD_(i)[k] of Equation 5 may be used as the cellvoltage.

In the step S4320, the first control unit 400 may determine the firstaverage cell voltage of (S4MAi[k], see Equation 1 and Equation 11) ofeach battery cell BC_(i) and the second average cell voltage(LMA_(i)[k], see Equation 3 and Equation 4), based on the time seriesdata of the cell voltage of each battery cell BC_(i) (see FIG. 4 b ).

The first average cell voltage (S4MAi[k]) may mean a short-term movementaverage of the cell voltage of each battery cell BC_(i) over a firstmoving window having a first time length. The second average cellvoltage (LMA_(i)[k]) may mean a long-term movement average of cellvoltages of each battery cell BC_(i) over a second moving window havinga second time length. When calculating the first average cell voltage(S4MAi[k]) and the second average cell voltage (LMA_(i)[k]), V_(i)[k] orVD_(i)[k] may be used.

In the step S4330, the first control unit 400 may determine a long-termand short-term average difference (|S4MAi[k]−LMA_(i)[k]|) of eachbattery cell BC_(i) (see FIG. 4 c ).

In the step S4340, the first control unit 400 may determine a celldiagnosis deviation (Ddiag,i[k]) of each battery cell BC_(i). The celldiagnosis deviation (Ddiag,i[k]) may mean deviation of the average value(|S4MAi[k]−LMA_(i)[k]|av) of the long-term and short-term averagedifferences for all battery cells and the long-term and short-termaverage difference (|S4MAi[k]−LMA_(i)[k]|) of the i^(th) battery cellBC_(i).

In the step S4350, the first control unit 400 may judge whether thediagnosis time has elapsed. The diagnosis time may be preset. If thejudgment of the step S4350 is YES, the step S4360 proceeds, and if thejudgment of the step S4350 is NO, the step S4310 to the step S4340 arerepeated again.

In the step S4360, the first control unit 400 may generate time seriesdata for the cell diagnosis deviation (Ddiag,i[k]) of each battery cellBC_(i) collected during the diagnosis time.

In the step S4370, the first control unit 400 may detect voltageabnormality of each battery cell BC_(i) by analyzing the time seriesdata for the cell diagnosis deviation (Ddiag,i[k]).

In one example, the first control unit 400 may integrate a time regionin which the cell diagnosis deviation (Ddiag,i[k]) is greater than thediagnosis threshold (e.g., 0.015) in the time series data for the celldiagnosis deviation (Ddiag,i[k]) of each battery cell BC_(i), and detecta battery cell establishing the condition that the integration time isgreater than a preset criterion time as a voltage abnormal cell.

For example, the first control unit 400 may integrate only a time regionin which the condition that the cell diagnosis deviation (Ddiag,i[k]) isgreater than the diagnosis threshold is continuously satisfied. If thecorresponding time region is plural, the first control unit 400 mayindependently calculate the integration time for each time region.

In another example, the first control unit 400 may integrate the numberof data in which the cell diagnosis deviation (Ddiag,i[k]) is greaterthan the diagnosis threshold (e.g., 0.015) in the time series data forthe cell diagnosis deviation (Ddiag,i[k]) of each battery cell BC_(i),and detect a battery cell establishing the condition that the dataintegration value is greater than a preset criterion count as a voltageabnormal cell.

The first control unit 400 may integrate only the number of dataincluded in the time region in which the condition that the celldiagnosis deviation (Ddiag,i[k]) is greater than the diagnosis thresholdis continuously satisfied. If the corresponding time region is plural,the first control unit 400 may independently integrate the number ofdata of each time region.

FIG. 21 is another flowchart exemplarily showing a method of detectingvoltage abnormality according to an embodiment of the presentdisclosure. The method of FIG. 21 may be periodically executed everyunit time by the first control unit 400.

In the method of detecting the voltage abnormality of FIG. 21 , the stepS4310 to the step S4360 are substantially the same as the embodiment ofFIG. 20 , and thus a description thereof will be omitted. After the stepS4360, the step S4380 proceeds.

In the step S4380, the first control unit 400 may generate time seriesdata of a statistically variable threshold (Dthreshold[k]) usingEquation 8. Inputs to the Sigma function of Equation 8 are time seriesdata for the cell diagnosis deviation (Ddiag,i[k]) of all battery cellsgenerated in the step S4360. Preferably, the maximum value of the celldiagnosis deviation (Ddiag,i[k]) may be excluded from the input value ofthe Sigma function. The cell diagnosis deviation (Ddiag,i[k]) is thedeviation from the average value for the long-term and short-termaverage differences (|SMA_(i)[k]−LMA_(i)[k]|).

In the step S4390, the first control unit 400 may generate the timeseries data of the filter diagnosis value (Dfilter,i[k]) by filteringthe cell diagnosis deviation (Ddiag,i[k]) of each battery cell BC_(i)using Equation 9.

In using Equation 9, D*diag,i[k] may be replaced with Ddiag,i[k].

In the step S4400, the first control unit 400 may judge voltageabnormality of each battery cell BC_(i) by analyzing the time seriesdata of the filter diagnosis value (Dfilter,i[k]).

In one example, the first control unit 400 may integrate a time regionin which the filter diagnosis value (Dfilter,i[k]) is greater than thediagnosis threshold (e.g., 0) in the time series data of the filterdiagnosis value (Dfilter,i[k]) for each battery cell BC_(i), and judge abattery cell establishing the condition that the integration time isgreater than the preset criterion time as a voltage abnormal cell.

Preferably, the first control unit 400 may integrate only a time regionin which the condition that the filter diagnosis value (Dfilter,i[k]) isgreater than the diagnosis threshold is successively satisfied. If thecorresponding time region is plural, the first control unit 400 mayindependently calculate the integration time for each time region.

In another example, the first control unit 400 may integrate the numberof data included in a time region in which the filter diagnosis value(Dfilter,i[k]) is greater than the diagnosis threshold (e.g., 0) in thetime series data of the filter diagnosis value (Dfilter,i[k]) for eachbattery cell BC_(i), and detect a battery cell establishing thecondition that the data integration value is greater than the presetcriterion count as a voltage abnormal cell.

Preferably, the first control unit 400 may integrate only the number ofdata included in a time region in which the condition that the filterdiagnosis value (Dfilter,i[k]) is greater than the diagnosis thresholdis continuously satisfied. If the corresponding time region is plural,the first control unit 400 may independently integrate the number ofdata of each time region.

FIG. 22 is still another flowchart exemplarily showing a method ofdetecting voltage abnormality according to an embodiment of the presentdisclosure. The method of FIG. 22 may be periodically executed everyunit time by the first control unit 400.

The battery diagnosing method according to the third embodiment issubstantially the same as the first embodiment, except that the stepsS4340, S4360 and S4370 are changed to the steps S4341, S4361 and S4371,respectively. Accordingly, only configurations with differences will bedescribed.

In the step S4341, the first control unit 400 may determine thenormalized cell diagnosis deviation (D*diag,i[k]) for the long-term andshort-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) of each batterycell BC_(i) using Equation 6. The normalization criterion value is theaverage value of the long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|). Equation 6 can be replaced with Equation 7.

In the step S4361, the first control unit 400 may generate time seriesdata for the normalized cell diagnosis deviation (D*diag,i[k]) of eachbattery cell BC_(i) collected during the diagnosis time (see FIG. 4 d ).

In the step S4371, the first control unit 400 may detect voltageabnormality of each battery cell BC_(i) by analyzing the time seriesdata for the normalized cell diagnosis deviation (D*diag,i[k]).

In one example, the first control unit 400 may integrate a time regionin which the cell diagnosis deviation (D*diag,i[k]) is greater than thediagnosis threshold (e.g., 4) in the time series data for the normalizedcell diagnosis deviation (D*diag,i[k]) of each battery cell BC_(i), anddetect a battery cell establishing the condition that the integrationtime is greater than the preset criterion time as a voltage abnormalcell.

The first control unit 400 may integrate only a time region in which thecondition that the normalized cell diagnosis deviation (D*diag,i[k]) isgreater than the diagnosis threshold is continuously satisfied. If thecorresponding time region is plural, the first control unit 400 mayindependently calculate the integration time for each time region.

In another example, the first control unit 400 may integrate the numberof data in which the cell diagnosis deviation is greater than thediagnosis threshold (e.g., 4) in the time series data for the normalizedcell diagnosis deviation (D*diag,i[k]) of each battery cell BC_(i), anddetect a battery cell establishing the condition that the dataintegration value is greater than the preset criterion count as avoltage abnormal cell.

The first control unit 400 may integrate only the number of dataincluded in the time region in which the condition that the normalizedcell diagnosis deviation (D*diag,i[k]) is greater than the diagnosisthreshold is continuously satisfied. If the corresponding time region isplural, the first control unit 400 may independently integrate thenumber of data of each time region.

FIG. 23 is still another flowchart exemplarily showing a method ofdetecting voltage abnormality according to an embodiment of the presentdisclosure. The method of FIG. 23 may be periodically executed everyunit time by the first control unit 400.

The battery diagnosing method according to FIG. 23 is substantially thesame as in FIG. 21 except that the steps S4340, S4360, S4380, S4390, andS4400 are changed to the steps S4341, S4361, S4381, S4391, and S4401,respectively, and the rest configuration is substantially the same.Accordingly, only a configuration different from that of FIG. 21 will bedescribed with respect to the embodiment of FIG. 23 .

In the step S4341, the first control unit 400 may determine thenormalized cell diagnosis deviation (D*diag,i[k]) for the long-term andshort-term average difference (|SMA_(i)[k]−LMA_(i)[k]|) of each batterycell BC_(i) using Equation 6. The normalization criterion value may meanan average value of the long-term and short-term average difference(|SMA_(i)[k]−LMA_(i)[k]|). Equation 6 can be replaced with Equation 7.

In the step S4361, the first control unit 400 may generate time seriesdata for the normalized cell diagnosis deviation (D*diag,i[k]) of eachbattery cell BC_(i) collected during the diagnosis time (see FIG. 4 d ).

In the step S4381, the first control unit 400 may generate time seriesdata of the statistically variable threshold (Dthreshold[k]) usingEquation 8. Inputs to the Sigma function of Equation 8 are time seriesdata for the normalized cell diagnosis deviation (D*diag,i[k]) of allbattery cells generated in the step S4361. According to an embodiment,at each time index, the maximum value of cell diagnosis deviation(D*diag,i[k]) may be excluded from the input value of the Sigmafunction.

In the step S4391, the first control unit 400 may generate time seriesdata the diagnosis values (Dfilter,i[k]) by filtering the cell diagnosisdeviation (D*diag,i[k]) of each battery cell BC_(i) based on thestatistically variable threshold (Dthreshold[k]) using Equation 9.

In the step S4401, the first control unit 400 may detect voltageabnormality of each battery cell BC_(i) by analyzing the time seriesdata of the filter diagnosis value (Dfilter,i[k]).

In one example, the first control unit 400 may integrate a time regionin which that the filter diagnosis value (Dfilter,i[k]) is greater thanthe diagnosis threshold (e.g., 0) in the time series data of the filterdiagnosis value (Dfilter,i[k]) for each battery cell BC_(i), and detecta battery cell establishing the condition that the integration time isgreater than the preset criterion time as a voltage abnormal cell.

For example, the first control unit 400 may integrate time regions inwhich the condition that the filter diagnosis value (Dfilter,i[k]) isgreater than the diagnosis threshold is successively satisfied. If thecorresponding time region is plural, the first control unit 400 mayindependently calculate the integration time for each time region.

In another example, the first control unit 400 may integrate the numberof data included in a time region in which the filter diagnosis value(Dfilter,i[k]) is greater than the diagnosis threshold (e.g., 0) in thetime series data of the filter diagnosis value (Dfilter,i[k]) for eachbattery cell BC_(i), and detect a battery cell establishing thecondition that the data integration value is greater than the presetcriterion count as a voltage abnormal cell.

The first control unit 400 may integrate only the number of dataincluded in the time region in which the condition that the filterdiagnosis value (Dfilter,i[k]) is greater than the diagnosis thresholdis continuously satisfied. If the corresponding time region is plural,the first control unit 400 may independently integrate the number ofdata of each time region.

FIG. 24 is still another flowchart exemplarily showing a method ofdetecting voltage abnormality according to an embodiment of the presentdisclosure.

In FIG. 24 , the step S4310 to the step S4361 are substantially the sameas in FIG. 23 . Accordingly, only the configuration different from thatof FIG. 23 will be described.

In the step S4410, the first control unit 400 may generate time seriesdata of the first moving average (SMA_(i)[k]) and time series data ofthe second moving average (LMA_(i)[k]) for the cell diagnosis deviation(D*diag,i[k]) using the time series data of the normalized celldiagnosis deviation (D*diag,i[k]) of each battery cell BC_(i) (see FIG.4 f ).

In the step S4420, the first control unit 400 may generate time seriesdata of the normalized cell diagnosis deviation (D*diag,i[k]) using tothe time series data of the first moving average (SMA_(i)[k]) and thetime series data of the second moving average (LMA_(i)[k]) of eachbattery cell BC_(i) using Equation 6 (see FIG. 4 g ).

In the step S4430, the first control unit 400 may generate time seriesdata of the statistically variable threshold (Dthreshold[k]) usingEquation 8 (see FIG. 4 g ).

In the step S4440, the first control unit 400 may generate time seriesdata for the filter diagnosis value (Dfilter,i[k]) of each battery cellBC_(i) based on the statistically variable threshold (Dthreshold[k])using Equation 9 (see FIG. 4 h ).

In the step S4450, the first control unit 400 may detect voltageabnormality of each battery cell BC_(i) by analyzing the time seriesdata of the filter diagnosis value (Dfilter,i[k]) of each battery cellBC_(i).

In one example, the first control unit 400 may integrate a time regionin which the filter diagnosis value (Dfilter,i[k]) is greater than thediagnosis threshold (e.g., 0) in the time series data of the filterdiagnosis value (Dfilter,i[k]) for each battery cell BC_(i), and detecta battery cell establishing the condition that the integration time isgreater than the preset criterion time as a voltage abnormal cell.

The first control unit 400 may integrate a time region in which thecondition that the filter diagnosis value (Dfilter,i[k]) is greater thanthe diagnosis threshold is successively satisfied. If the correspondingtime region is plural, the first control unit 400 may independentlycalculate the integration time for each time region.

The first control unit 400 may integrate the number of data included inthe time region in which the filter diagnosis value (Dfilter,i[k]) isgreater than the diagnosis threshold (e.g., 0) in the time series dataof the filter diagnosis value (Dfilter,i[k]) for each battery cellBC_(i), and detect a battery cell in which the data integration value isgreater than the preset criterion count as a voltage abnormal cell.

The first control unit 400 may integrate only the number of dataincluded in the time region in which the condition that the filterdiagnosis value (Dfilter,i[k]) is greater than the diagnosis thresholdis continuously satisfied. If the corresponding time region is plural,the first control unit 400 may independently integrate the number ofdata of each time region.

In FIG. 24 , the first control unit 400 may recursively perform the stepS4410 and the step S4420 twice or more. The first control unit 400 maygenerate time series data of the first moving average (SMA_(i)[k]) andtime series data of the second moving average (LMA_(i)[k]) for the celldiagnosis deviation (D*diag,i[k]) in the step S4410 again by using thetime series data of the normalized cell diagnosis deviation(D*diag,i[k]) generated in the step S4420.

The first control unit 400 may generate time series data of thenormalized cell diagnosis deviation (D*diag,i[k]) based on Equation 6again by using the time series data of the first moving average(SMA_(i)[k]) and the time series data of the second moving average(LMA_(i)[k]) of each battery cell BC_(i) in the step S4420. Such arecursive algorithm may be repeated a predetermined number of times.

When the step S4410 and the step S4420 are performed according to therecursive algorithm, the step S4430 to the step S4450 may be implementedusing the time series data of the cell diagnosis deviation (D*diag,i[k])finally calculated through the recursive algorithm.

In an embodiment of the present disclosure, the first control unit 400may detect voltage abnormality for all battery cells, and then, whenvoltage abnormality is detected in a specific battery cell(s), the firstcontrol unit 400 may generate third information including the detectionresult information. Also, the first control unit 400 may record theidentification information ID of the battery cell in which the voltageabnormality is diagnosed, the time point in which the voltageabnormality is detected, and the detection flag in a memory unit (notshown).

The third information may include a message indicating that there is acell with voltage abnormality in the cell group CG. Optionally, thethird information may include a warning message indicating that adetailed check of the battery cells BC₁ to BC_(N) is required.

According to the above-described embodiments, for each unit time, twomoving averages of the cell voltage of each battery cell for twodifferent time lengths are determined, and based on the differencebetween the two moving averages of each of the plurality of batterycells, voltage abnormality of each battery cell may be efficiently andaccurately detected.

According to another aspect, voltage abnormality of each battery cellmay be accurately detected by applying an advanced technique such asnormalization and/or statistically variable threshold in analyzing thedifference in the change trend of two moving averages of each batterycell.

According to still another aspect, it is possible to precisely detectthe time region in which the voltage abnormality of each battery celloccurs and/or the voltage abnormality detection count by analyzing thetime series data of the filter diagnosis value determined based on thestatistically variable threshold.

As described above, the method for detecting voltage abnormality by thefirst control unit 400 included in the battery cell diagnosing apparatus1000 has been reviewed. Hereinafter, a method for detecting behaviorabnormality by the first control unit 400 included in the battery celldiagnosing apparatus 1000 will be reviewed.

FIGS. 25 and 26 are flowcharts illustrating in detail a process ofdetecting behavior abnormality by the battery cell diagnosing apparatus1000 according to an embodiment of the present disclosure. FIG. 25 is aflowchart exemplarily showing a method of detecting behavior abnormalityaccording to an embodiment of the present disclosure.

In the step S5710, the first control unit 400 determines a plurality ofsub voltage curves by applying the moving window of the first timelength A to the criterion voltage curve C2. The criterion voltage curveC2 is a time series of a plurality of voltage values representing thecell voltage of the battery cell BC measured at each sampling time for apredetermined period (t1 to tM).

In the step S5720, the first control unit 400 determines a voltagedeviation (ΔV[K]) associated with each sub voltage curve SK of theplurality of sub voltage curves. The step S5720 may include the stepsS5722, S5724 and S5726 as sub-steps.

In the step S5722, the first control unit 400 may determine a long-termaverage voltage value (Vav1[K]) of the sub voltage curve SK by using thefirst average filter of the first time length A (see Equation 10).

In the step S5724, the first control unit 400 may determine a short-termaverage voltage value (Vav2[K]) of the sub voltage curve SK by using thesecond average filter of the second time length B (see Equation 11).

In the step S5726, the first control unit 400 may determine a voltagedeviation (ΔV[K]) by subtracting the other from one of the long-termaverage voltage value (Vav1[K]) and the short-term average voltage value(Vav2[K]).

In the step S5730, the first control unit 400 may judge whether thebattery cell BC has behavior abnormality by comparing each of theplurality of voltage deviations determined for the plurality of subvoltage curves with at least one of a first threshold deviation and asecond threshold deviation. When the value of the step S5730 is “Yes”,the process may proceed to the step S5740.

In the step S5740, the first control unit 400 may detect behaviorabnormality of the battery cell BC.

FIG. 26 is another flowchart exemplarily showing a method of detectingbehavior anomalies according to an embodiment of the present disclosure.

In the step S5800, the first control unit 400 may determine a pluralityof sub current curves by applying the moving window of the first timelength A to the criterion current curve C3. The criterion current curveC3 is a time series of a plurality of current values representing thebattery current of the battery cell BC measured at each sampling timefor a predetermined period (t1 to tM).

In the step S5810, the first control unit 400 may determine a pluralityof sub voltage curves by applying the moving window of the first timelength A to the criterion voltage curve C2. The step S5810 is the sameas the step S5710.

In the step S5812, the first control unit 400 may determine a currentchange amount of each sub current curve RK of the plurality of subcurrent curves.

In the step S5820, the first control unit 400 may determine a voltagedeviation (ΔV[K]) of each sub voltage curve SK associated with each subcurrent curve RK in which the current change amount is equal to or lessthan a threshold change amount among the plurality of sub voltagecurves. The step S5820 may include the steps S5722, S5724, and S5726 ofFIG. 25 .

In the step S5830, the first control unit 400 may judge whether thebattery cell BC has behavior abnormality by comparing each voltagedeviation determined in the step S5820 with at least one of a firstthreshold deviation and a second threshold deviation. If the value ofthe step S5830 is “Yes”, the process proceeds to the step S5840.

In the step S5840, the first control unit 400 may detect behaviorabnormality of the battery cell BC.

FIGS. 27 to 30 are flowcharts in detail illustrating a process in whichthe external device 2000 detects lithium precipitation abnormality whilerepeatedly performing a charging and discharging cycle using the firstinformation according to an embodiment of the present disclosure.

The external device 2000 may detect lithium precipitation abnormalityaccording to an embodiment of the present disclosure as shown in theflowcharts of FIGS. 27 to 30 , and generate second information includingthe detected result.

FIG. 27 is a flowchart exemplarily showing a method of detecting lithiumprecipitation abnormality according to an embodiment of the presentdisclosure.

First, the external device 2000 initializes the charging and dischargingcycle index k to 1 in the step S7010, and initializes the first capacitydifference change amount (ΔdAh[1]) and the first accumulated capacitydifference change amount (Σ_(i=1) ¹ ΔdAh) to 0 in the step S7020,respectively.

Subsequently, the external device 2000 may start the first charging anddischarging cycle for the battery in the step S7030. In thisspecification, when the external device 2000 starts the charging anddischarging cycle, it may mean that data corresponding to the chargingand discharging cycle is obtained using the first information.

Subsequently, the external device 2000 may calculate the chargingcapacity (ChgAh[1]) and the discharging capacity (DchgAh[1]) using thecurrent measurement value included in the first information during thefirst charging and discharging cycle in the step S7040.

The first information may include information on the charging cycleperformed in a preset charging voltage region and the discharging cycleperformed in a preset discharging voltage region.

The charging voltage region and the discharging voltage region may bethe same or different. Preferably, the discharging cycle is initiatedafter the battery cell voltage is stabilized after the charging cycle isfinished. Also, the discharging cycle may be ended when the battery cellvoltage reaches the preset discharging end voltage or when theintegrated value of the discharging current reaches the presetdischarging capacity. When the start and end of the charging cycle andthe discharging cycle are controlled based on the voltage value, theexternal device 2000 may refer to the voltage measurement value includedin the first information. The voltage measurement value included in thefirst information may be a value measured through the voltage sensingunit 200.

In the step S7050, the external device 2000 may determine a capacitydifference (dAh[1]) corresponding to the difference between the chargingcapacity (ChgAh[1]) and the discharging capacity (DchgAh[1]).

The external device 2000 may record the determined capacity difference(dAh[1]) together with a time stamp in the storage unit 2100. In oneexample, the capacity difference (dAh[I]) may be determined bysubtracting the discharging capacity (DchgAh[1]) from the chargingcapacity (ChgAh[1]).

In the step S7060, the external device 2000 may judge whether the indexk for the charging and discharging cycle is equal to n. n is a presetnatural number, which is the total number of charging and dischargingcycles that can proceed to detect lithium precipitation abnormality. Inone example, n may be 20.

If the judgment of the step S7060 is YES, the external device 2000 mayterminate the process for detecting lithium precipitation abnormality.On the other hand, if the judgment of the step S7060 is NO, the externaldevice 2000 may move the process to S7070.

In the step S7070, the external device 2000 may start a second chargingand discharging cycle. The condition of the second charging anddischarging cycle may be substantially the same as that of the firstcharging and discharging cycle.

Subsequently, the external device 2000 may determine the chargingcapacity (ChgAh[2]) and the discharging capacity (DchgAh[2]) of thesecond charging and discharging cycle for the battery in the step S7080,and determine the capacity difference (dAh[2]) corresponding to thedifference between the charging capacity (ChgAh[2]) and the dischargingcapacity (DchgAh[2]) in the step S7090.

Subsequently, the external device 2000 may determine the second capacitydifference change amount (ΔdAh[2]) by subtracting the capacitydifference (dAh[2]) of the second charging and discharging cycle fromthe capacity difference (dAh[1]) of the first charging and dischargingcycle in the step S7100. After the step S7100, the step S7110 of FIG. 28may be performed.

FIG. 28 is another flowchart exemplarily showing a method of detectinglithium precipitation abnormality according to an embodiment of thepresent disclosure.

The external device 2000 may judge whether the second capacitydifference change amount (ΔdAh[2]) is greater than the criterion valuein the step S7110. The criterion value may be 0.

If the judgment of the step S7110 is YES, the external device 2000 mayupdate the accumulated capacity difference change amount by adding thesecond capacity difference change amount (ΔdAh[2]) to the firstaccumulated capacity difference change amount (Σ_(i=1) ¹ ΔdAh) in thestep S7120, and determine the updated value as the second accumulatedcapacity difference change amount (Σ_(i=1) ² ΔdAh). The firstaccumulated capacity difference change amount (Σ_(i=1) ¹ ΔdAh) may be 0that is an initialization value.

On the other hand, if the judgment of the step S7110 is NO, the initialvalue 0 may be assigned to the second accumulated capacity differencechange amount (Σ_(i=1) ² ΔdAh) without adding the second capacitydifference change amount (ΔdAh[2]) to the first accumulated capacitydifference change amount (Σ_(i=1) ¹ ΔdAh).

The external device 2000 may judge whether the second accumulatedcapacity difference change amount (Σ_(i=1) ² ΔdAh) is greater than orequal to a threshold value in the step S7140. The threshold value maymean a value suitable for detecting lithium precipitation abnormality.For example, the threshold value may be 0.1% of the battery capacity.The threshold value may be a value preset in the external device 2000 ora value included in the first information.

If the judgment of the step S7140 is YES, the external device 2000 maydetect lithium precipitation abnormality in the step S7150.

If the judgment of the step S7140 is NO, that is, if the secondaccumulated capacity difference change amount (Σ_(i=1) ² ΔdAh) is lessthan the threshold value (or, is 0), the external device 2000 may judgewhether the index k for the charging and discharging cycle is equal to nin the step S7160. Here, n is the total number of charging anddischarging cycles that can be performed to detect lithium precipitationabnormality.

If the judgment of the step S7160 is YES, the external device 2000 mayfinally judge that lithium precipitation abnormality does not occur inthe battery and terminate the process because the charging anddischarging cycle for detecting lithium precipitation is completed.

The external device 2000 may output that lithium precipitationabnormality is not detected through the second information. For example,the second information may include a message indicating that lithiumprecipitation abnormality has not occurred.

On the other hand, if the judgment of the step S7160 is NO, the externaldevice 2000 may further proceed with the charging and discharging cycleto detect lithium precipitation abnormality. After the step S7160, thestep S7180 of FIG. 29 is performed.

FIG. 29 is still another flowchart exemplarily showing a method ofdetecting lithium precipitation abnormality according to an embodimentof the present disclosure.

In the step S7180, the external device 2000 starts the third chargingand discharging cycle. The condition of the third charging anddischarging cycle may be substantially the same as that of the firstcharging and discharging cycle.

The external device 2000 may determine the charging capacity (ChgAh[3])and the discharging capacity (DchgAh[3]) during the third charging anddischarging cycle for the battery in the step S7190, and, determine thecapacity difference (dAh[3]) corresponding to the difference between thecharging capacity (ChgAh[3]) and the discharging capacity (DchgAh[3]) inthe step S7200.

The external device 2000 may determine the third capacity differencechange amount (ΔdAh[3]) by subtracting the capacity difference (dAh[3])of the third charging and discharging cycle from the capacity difference(dAh[2]) of the second charging and discharging cycle in the step S7210.

The external device 2000 may judge whether the third capacity differencechange amount (ΔdAh[3]) is greater than the criterion value in the stepS7220. For example, the criterion value may be 0.

If the judgment of the step S7220 is YES, the external device 2000 mayupdate the accumulated capacity difference change amount by adding thethird capacity difference change amount (ΔdAh[3]) to the secondaccumulated capacity difference change amount (Σ_(i=1) ² ΔdAh) in thestep S7230, and determine the updated value as the third accumulatedcapacity difference change amount (Σ_(i=1) ³ ΔdAh).

On the other hand, if the judgment of the step S7220 is NO, the externaldevice 2000 may assign the initial value 0 to the third accumulatedcapacity difference change amount (Σ_(i=1) ³ ΔdAh) without adding thethird capacity difference change amount (ΔdAh[3]) to the secondaccumulated capacity difference change amount (Σ_(i=1) ² ΔdAh) in thestep S7240.

After the step S7230 and the step S7240, the step S7250 may beperformed.

In the step S7250, the external device 2000 may judge whether the thirdaccumulated capacity difference change amount (Σ_(i=1) ³ ΔdAh) isgreater than or equal to a threshold value.

If the judgment of the step S7250 is YES, the external device 2000 maydetect lithium precipitation abnormality inside the battery in the stepS7260.

The external device 2000 may terminate the process after detectinglithium precipitation abnormality in the step S7260.

If the judgment of the step S7250 is NO, that is, if the thirdaccumulated capacity difference change amount (Σ_(i=1) ³ ΔdAh) is lessthan the threshold value (or, is 0), the external device 2000 may judgewhether the index k for the charging and discharging cycle is equal to nin the step S7270. Here, n is the total number of charging anddischarging cycles that can be performed to detect whether lithiumprecipitation has occurred inside the battery.

If the judgment of the step S7270 is YES, the charging and dischargingcycle for detecting lithium precipitation abnormality has beencompleted, so it is judged that no lithium precipitation abnormality hasoccurred in the battery and the process may be terminated.

The external device 2000 may generate the second information after theprocess is terminated. The external device 2000 may generate a warningmessage indicating that lithium precipitation has been detected in thesecond information. Alternatively, the second information may include amessage indicating that lithium precipitation abnormality is notdetected.

On the other hand, if the judgment of the step S7270 is NO, the externaldevice 2000 may further proceed with the charging and discharging cycleto detect lithium precipitation abnormality.

The detection logic for the lithium precipitation abnormality that theexternal device 2000 proceeds in the fourth charging and dischargingcycle and the subsequent charging and discharging cycle is substantiallythe same as described above.

FIG. 30 is still another flowchart exemplarily showing a method ofdetecting lithium precipitation abnormality according to an embodimentof the present disclosure. Hereinafter, a process performed by theexternal device 2000 in the fourth to n^(th) charging and dischargingcycles will be generalized and described with reference to FIG. 30 .

In the step S7280, the external device 2000 starts the K^(th) chargingand discharging cycle (k is a natural number of 4 to n). The conditionof the K^(th) charging and discharging cycle is substantially the sameas that of the first charging and discharging cycle.

Subsequently, the external device 2000 determines the charging capacity(ChgAh[k]) and the discharging capacity (DchgAh[k]) during the K^(th)charging and discharging cycle for the battery in the step S7290, anddetermines the capacity difference (dAh[k]) corresponding to thedifference between the charging capacity (ChgAh[k]) and the dischargingcapacity (DchgAh[k]).

Subsequently, the external device 2000 determines the K^(th) capacitydifference change amount (ΔdAh[k]) by subtracting the capacitydifference (dAh[k]) of the K^(th)charging and discharging cycle from thecapacity difference (dAh[k−1]) of the k−1^(th) charging and dischargingcycle in the step S7310.

Subsequently, the external device 2000 judges whether the K^(th)capacity difference change amount (ΔdAh[k]) is greater than thecriterion value in the step S7320. Preferably, the criterion value maybe 0.

If the judgment of the step S7320 is YES, the external device 2000 mayupdate the accumulated capacity difference change amount by adding theK^(th) capacity difference change amount (ΔdAh[k]) to the k−1^(th)accumulated capacity difference change amount (Σ_(i=1) ^(k-1) ΔdAh) inthe step S7330, and determine the updated value as the K^(th)accumulated capacity difference change amount (Σ_(i=1) ^(k) ΔdAh).

On the other hand, if the judgment of the step S7320 is NO, the externaldevice 2000 may assign the initial value of 0 to the K^(th) accumulatedcapacity difference change amount (Σ_(i=1) ^(k) ΔdAh) without adding theK^(th) capacity difference change amount (ΔdAh[k]) to the k−1^(th)accumulated capacity difference change amount (Σ_(i=1) ^(k-1) ΔdAh) inthe step S7340.

The step S7350 proceeds after the step S7330 and the step S7340.

In the step S7350, the external device 2000 judges whether theK^(th)accumulated capacity difference change amount (Σ_(i=1) ^(k) ΔdAh)is greater than or equal to a threshold value.

If the judgment in the step S7350 is YES, the external device 2000 mayjudge that lithium precipitation abnormality is detected in the batteryand terminate the process in the step S7360.

If the judgment of the step S7350 is NO, that is, if the K^(th)accumulated capacity difference change amount (Σ_(i=1) ^(k) ΔdAh) isless than the threshold value (or, is 0), the external device 2000 mayjudge whether the index k for the charging and discharging cycle isequal to n in the step S7370. Here, n is the total number of chargingand discharging cycles that can be performed to detect whether lithiumprecipitation has occurred inside the battery.

If the judgment of the step S7370 is YES, the charging and dischargingcycle for detecting lithium precipitation is completed, so it is finallyjudged that no lithium precipitation abnormality has occurred in thebattery and the process may be terminated.

On the other hand, if the judgment of the step S7370 is NO, the externaldevice 2000 returns the process to the step S7280 in order to furtherproceed with the charging and discharging cycle to detect lithiumprecipitation abnormality. Accordingly, the steps S7280 to S7370 may beperiodically repeated until the index k of the charging and dischargingcycle becomes n.

According to an embodiment of the present disclosure, if the capacitydifference change amount calculated in the current charging anddischarging cycle is equal to or less than the criterion value, theaccumulated capacity difference change amount calculated up to theprevious cycle may be initialized to 0. In addition, if the capacitydifference change amount calculated in the current charging anddischarging cycle is greater than the criterion value, the currentcapacity difference change amount may be added to the previousaccumulated capacity difference change amount. As a result, theaccumulated capacity difference change amount increases. The previousaccumulated capacity difference change amount has a value of 0 or apositive value. If it has a positive value, the capacity differencechange amount exceeding the criterion value calculated in successivecharging and discharging cycles may be integrated.

In addition, when the capacity difference change amount is integratedand the capacity difference change amount decreases to the criterionvalue or less in a specific charging and discharging cycle, theaccumulated capacity difference change amount may be initialized to 0.By applying this logic, the accumulated capacity difference changeamount corresponds to a quantitative indicator that measures a kind oflithium precipitation abnormality. That is, if the capacity differencechange amount is greater than the criterion value, it may mean thatthere is possibility of lithium precipitation.

In addition, if the accumulated capacity difference change amountincreases to the threshold value or more as the condition that thecapacity difference change amount exceeds the criterion value in aplurality of charging and discharging cycles consecutive in time seriesis successively satisfied, it may mean that the possibility of lithiumprecipitation is high. The present disclosure has technical significancein that the possibility of lithium precipitation is quantified using thefactor of the accumulated capacity difference change amount.

FIG. 31 is a graph showing changes in data measured in an experimentalexample to which a method for the external device 2000 to detect whetherlithium precipitation according to an embodiment of the presentdisclosure occurs is applied.

In this experimental example, a pouch-type lithium polymer battery wasused. The lithium polymer battery selected for the experiment isdegraded and thus is in a state in which lithium has started toprecipitate on the negative electrode. The current capacity of thelithium polymer battery, which reflects the degree of degradation, isapproximately 50 Ah. The charging condition of the charging cycle isCC-CV charging. When the CC charging target voltage is reached, CCcharging is terminated and converted to CV charging, and the charging isterminated when the CV charging current reaches the target current. Thedischarging condition of the discharging cycle is CC discharging, andthe discharging is terminated when discharging is performed as much as agiven discharging capacity. The temperature condition of the chargingcycle and the discharging cycle is 45° C. The criterion value fordetermining whether to integrate the capacity difference change amountis set to 0, and the threshold value for diagnosing lithiumprecipitation abnormality is set to 0.06 Ah.

Graph {circle around (1)} is a graph showing measurement results of thecharging capacity (ChgAh[k]) and the discharging capacity (DChgAh[k])for each charging and discharging cycle. The charging capacity(ChgAh[k]) and the discharging capacity (DChgAh[k]) are calculated byintegrating the current value measured through the sense resistor. Dueto an error in the discharging current measurement value, thedischarging capacity is greater than the charging capacity from thefourth discharging cycle.

Graph {circle around (2)} is a graph showing the capacity difference(dAh[k]) for each charging and discharging cycle. Referring to Graph{circle around (1)}, since the discharging capacity is greater than thecharging capacity from the fourth charging and discharging cycle, thecapacity difference (dAh[k]) becomes negative from the fourth cycle.

Graph 3 is a graph showing the capacity difference change amount(ΔdAh[k]) for each charging and discharging cycle. Indices of thecharging and discharging cycles in which the capacity difference changeamount (ΔdAh[k]) is positive are 2 to 13, 17, 18, and 20. The indices ofthe charging and discharging cycle in which the capacity differencechange amount (ΔdAh[k]) is negative are 14 to 16 and 19.

Graph {circle around (4)} is a graph showing the accumulated capacitydifference change amount (Σ_(i=1) ^(k) ΔdAh) for each charging anddischarging cycle. Indices of the charging and discharging cycles inwhich the capacity difference change amount (ΔdAh[k]) is positive are 2to 13. Therefore, the accumulated capacity difference change amount(Σ_(i=1) ^(k) dAh) increases as the capacity difference change amount(ΔdAh[k]) of the second to 13^(th) charging and discharging cycles isintegrated. In addition, when accumulated up to the capacity differencechange amount of the 13^(th) charging and discharging cycle, theaccumulated capacity difference change amount (Σ_(i=1) ^(k) ΔdAh)exceeds the threshold value of 0.06 Ah. Therefore, the external device2000 proceeds up to the 13^(th) charging and discharging cycle, thenjudges that lithium precipitation abnormality has occurred inside thebattery, outputs the lithium precipitation abnormality detection resultthrough the second information, and terminates the detection process.Since lithium is precipitated in the negative electrode of the lithiumpolymer battery used in this experiment, it can be seen that thedetection accuracy of the present disclosure is high.

FIG. 32 is a graph showing changes in data measured in anotherexperimental example to which a method for detecting lithiumprecipitation abnormality according to an embodiment of the presentdisclosure is applied.

In FIG. 32 , Graph {circle around (1)} is the same as Graph {circlearound (1)} of the above-described experimental example. Graph {circlearound (1)}′ is a graph showing the measurement results of the chargingcapacity (ChgAh[k]) and the discharging capacity (DchgAh[k]) when usinga current measurement means having a different current measurement valueerror from the experimental example described above. In thisexperimental example, the error of the discharging current measurementvalue is larger than that of the above-described experimental example.Therefore, the discharging capacity (DchgAh[k]) graph is shifted upwardcompared to the above-described experimental example.

Graphs {circle around (2)} and {circle around (2)}′ are graphs showingthe capacity difference (dAh[k]) for each charging and dischargingcycle, Graphs {circle around (3)} and {circle around (3)}′ are graphsshowing the capacity difference change amount (ΔdAh[k]) for eachcharging and discharging cycle, and Graphs {circle around (4)} and{circle around (4)}′ are graphs showing the accumulated capacitydifference change amount (Σ_(i=1) ^(k) ΔdAh) for each charging anddischarging cycle.

Graphs {circle around (2)}, {circle around (3)} and {circle around (4)}are calculated using the data of Graph {circle around (1)}, and Graphs{circle around (2)}′, {circle around (3)}′ and {circle around (4)}′ arecalculated using the data of Graph {circle around (1)}′.

As shown in FIG. 32 , Graphs {circle around (2)}, {circle around (3)}and {circle around (4)} and Graphs {circle around (2)}′, {circle around(3)}′ and {circle around (4)}′ are substantially the same. Therefore,the external device 2000 proceeds up to the 13^(th) charging anddischarging cycle regardless of the magnitude of the error even if thedischarging current value includes a measurement error, then judges thatlithium precipitation abnormality has occurred inside the battery,outputs the lithium precipitation abnormality detection result throughthe second information, and terminates the detection process. From theexperimental results, it can be seen that the present disclosure canreliably detect lithium precipitation abnormality regardless of theerror of the current measurement value.

FIG. 33 is a flowchart in which the battery cell diagnosing apparatus1000 according to an embodiment of the present disclosure diagnoses anabnormal state of a battery cell using the external device 2000. Thefeatures identical to the former embodiment will be not described indetail.

In the step S7000 of FIG. 33 , the external device 2000 may detectwhether there is a parallel connection abnormality. This will bedescribed in detail with reference to FIG. 34 .

FIG. 34 is a flowchart exemplarily showing a battery diagnosing methodaccording to an embodiment of the present disclosure. The method of FIG.34 may be repeatedly executed at a first time interval.

Referring to FIG. 34 , in the step S7610, the external device 2000 maycollect charging and discharging data of the battery B included in thefirst information.

In the step S7620, the external device 2000 may determine an estimatedcapacity value representing the full charging capacity of the battery B.The step S7620 may include steps S7622 and S7624. In the step S7622, theexternal device 2000 may determine a current integration value and SOCchange value of the battery B by inputting the charging and dischargingdata to the capacity estimation model. In the step S7624, the externaldevice 2000 may determine an estimated capacity value representing thefull charging capacity of the battery B from the ratio between thecurrent integration value and the SOC change value of the battery B. Theexternal device 2000 may store time series of the estimated capacityvalues.

In the step S7630, the external device 2000 may detect an abnormality inthe parallel connection B200 by monitoring the change over time of theestimated capacity value. The step S7630 may include steps S7632, S7634,S7636, S7638, and S7639. In the step S7632, the external device 2000 maydetermine a threshold capacity value for the second time to be smallerthan the estimated capacity value for the first time. For example, thesecond time may be a timing at which the current estimated capacityvalue is calculated, and the first time may be a timing at which theestimated capacity value 10 times ago is calculated. In the step S7634,the external device 2000 may compare the estimated capacity value at thesecond time with the threshold capacity value for the second time. Ifthe estimated capacity value at the second time is less than thethreshold capacity value for the second time, it indicates that at leastone of the first capacity abnormality and the second capacityabnormality has occurred in the parallel connection B200. If the valueof the step S7634 is “yes”, the process proceeds to the step S7636.Otherwise, the process may proceed to the step S7638. In the step S7636,the external device 2000 may increase the diagnosis count by 1. In thestep S7638, the external device 2000 may reset the diagnosis count. Inthe step S7639, the external device 2000 may determine whether thediagnosis count reaches a threshold count. If the value of the stepS7639 is “Yes”, it indicates that at least one unit cell UC of theparallel connection B200 is detected as having a second capacityabnormality, which is a complete disconnection failure.

In the step S7640, the external device 2000 may detect a parallelconnection abnormality. The external device 2000 may determine thenumber of unit cells in which a parallel connection abnormality isdetected. The external device 2000 may determine the number of unitcells having complete disconnection failure among the plurality of unitcells UC1 to UCM, from two estimated capacity values at the past twotimes (e.g., te, tf) with a time interval of the second time interval orless, at which the maximum reduction of the estimated capacity valueappears.

The external device 2000 may determine the number of unit cells having acomplete disconnection failure among the plurality of unit cells UC1 toUCM. The number of abnormal unit cells may be determined to be equal toa maximum integer not greater than ΔAhmax/(Ahp/M). Ahp is an estimatedcapacity value at an earlier time (te) of two times (e.g., te, tf).ΔAhmax is a maximum reduction amount of the full charging capacity overtwo times (e.g., te, tf) prior to the timing when an abnormality of theparallel connection 200 is detected, and is a result obtained bysubtracting the estimated capacity value at the later time (tf) from theestimated capacity value at the previous time (te). For example, whenAhp=122 Ah, ΔAhmax=27 Ah, and M=10, since 2≤27 Ah/(122 Ah/10)<3, thenumber of abnormal unit cells may be determined to be 2.

FIG. 35 is a flowchart in which the battery cell diagnosing apparatus1000 according to an embodiment of the present disclosure diagnoses anabnormal state of a battery cell using the external device 2000. Thefeatures identical to the former embodiment will be not described indetail.

In the step S7000 of FIG. 35 , the external device 2000 may detectwhether there is an internal short circuit abnormality. This will bedescribed in detail with reference to FIGS. 36 and 37 .

FIGS. 36 and 37 are flowcharts specifically showing a process ofdetecting an internal short circuit abnormality while the externaldevice 2000 repeatedly performs a charging and discharging cycle usingthe first information according to an embodiment of the presentdisclosure.

According to the flowcharts shown in FIGS. 36 and 37 , the externaldevice 2000 may detect an internal short circuit abnormality accordingto an embodiment of the present disclosure and generate secondinformation including the detected result.

FIG. 36 is a flowchart exemplarily showing a battery management methodaccording to an embodiment of the present disclosure. The method of FIG.36 is for detecting an internal short circuit abnormality of the batterycell BC based on the SOC trends of all of the plurality of battery cellsBC₁ to BC_(N) monitored in a recent charging period. For convenience ofexplanation, it is assumed that the recent charging period is from thetime point t4 to the time point t5.

Referring to FIG. 36 , in the step S7610, the external device 2000 maydetermine a first SOC change, which is a difference between a first SOCat a first charging time point and a second SOC at a second chargingtime point, by applying the SOC estimation algorithm to each stateparameter of each of the plurality of battery cells BC₁ to BC_(N) duringcharging of the battery pack 10, obtained using the first information,for each battery cell BC. The first charging time point and the secondcharging time point are not particularly limited as long as they are twodifferent time points within the recent charging period.

For example, the first charging time point may be a start time point t4of the recent charging period, and the second charging time point may bean end time point t5 of the recent charging period. Since the method ofFIG. 36 relates to charging, the first SOC change represents an increasein SOC from the first charging time point to the second charging timepoint. For example, referring to FIG. 18 , the first SOC change of theabnormal battery cell is the difference between the first SOC VC54 andthe second SOC VC55.

In the step S7620, the external device 2000 determines a criterionfactor by applying a statistical algorithm to the first SOC changes ofat least two battery cells among the plurality of battery cells BC₁ toBC_(N). The criterion factor may be equal to an average or a medianvalue of the first SOC changes of at least two battery cells among theplurality of battery cells BC₁ to BC_(N). For example, referring to FIG.18 , when the curve VC4 is an average of the first SOC changes, thecriterion factor is a difference between the SOC VC44 and the SOC VC45.

In the step S7630, the external device 2000 detects an internal shortcircuit abnormality by comparing the first SOC change with a criterionfactor for each battery cell BC. In detecting the internal short circuitabnormality, one or a combination of two or more of the followingdetection conditions may be utilized.

-   -   [Condition #1: The first SOC change must be less than the        criterion factor by a threshold value TH1 or more]    -   [Condition #2: The ratio of the first SOC change to the        criterion factor must be equal to or less than a critical value        TH2, where TH2 is 0 to 1]    -   [Condition #3: The ratio of the first SOC change to the        criterion factor must be less than the previous ratio by a        threshold value TH3 or more]

In Condition #3, the previous ratio is a ratio of the first SOC changeto the criterion factor in the charging period preceding the recentcharging period (t0 to t1 in FIG. 17 ).

The threshold values TH1, TH2, TH3 may be predetermined fixed values.Alternatively, the external device 2000 may determine at least one ofthe threshold values TH1, TH2, TH3 based on the integrated value of thecharging current measured over the period from the first charging timepoint to the second charging time point.

Whenever the charging mode of the battery pack 10 resumes, at least oneof the threshold values TH1, TH2, TH3 may be newly updated. For example,the external device 2000 may obtain a target value of SOC change (e.g.,60%) by dividing the integration value of the charging current (e.g., 3Ah [ampere-hour]) by the design capacity of the battery cell BC (e.g., 5Ah), and determine at least one of the threshold values TH1, TH2, TH3 bymultiplying the ratio of the criterion factor to the target value by apredetermined scaling constant (which is a positive value). The scalingconstant used to determine any one of the threshold values TH1, TH2, TH3may be different from a scaling constant used to determine another oneof the threshold values TH1, TH2, TH3. The target value may bedetermined during at least one of the steps S7610, S7620, and S7630. Atleast one of the threshold values TH1, TH2, TH3 may be determined duringat least one of the steps S7620 and S7630.

When all of the plurality of battery cells BC₁ to BC_(N) are normal, thetarget value and the criterion factor may be substantially equal to eachother. Meanwhile, as the number of battery cells having an internalshort circuit abnormality among the plurality of battery cells BC₁ toBC_(N) increases, the criterion factor greatly decreases from the targetvalue. Accordingly, by determining at least one of the threshold valuesTH1, TH2, TH3 according to the above method, the accuracy of detectingan internal short circuit abnormality may be improved.

Meanwhile, after the target value is determined before the step S7620,in the step S7620, only first SOC changes less than or equal to thetarget value among all first SOC changes of the plurality of batterycells BC₁ to BC_(N) may be used to determine the criterion factor. Inthis case, in determining the criterion factor, among all first SOCchanges of the plurality of battery cells BC₁ to BC_(N), first SOCchanges exceeding the target value are excluded, so the battery cell BChaving a relatively serious internal short circuit abnormality may bedetected preferentially from the plurality of battery cells BC₁ toBC_(N).

FIG. 37 is another flowchart exemplarily showing a battery managementmethod according to an embodiment of the present disclosure. The methodof FIG. 37 is for detecting an internal short circuit abnormality of thebattery cell BC based on the SOC trends of all of the plurality ofbattery cells BC₁ to BC_(N) monitored in a recent discharging period anda recent charging period, respectively. For convenience of explanation,it is assumed that the recent charging period is from time point t4 totime point t5, and the recent discharging period is from time point t6to time point t7.

Referring to FIG. 37 , in the step S7710, the external device 2000 maydetermine a first SOC change, which is a difference between a first SOCat a first charging time point and a second SOC at a second chargingtime point, by applying the SOC estimation algorithm to state parametersof each of the plurality of battery cells BC₁ to BC_(N), obtained duringcharging of the battery pack 10 using the first information, for eachbattery cell BC. The first charging time point and the second chargingtime point are not particularly limited as long as they are twodifferent time points within the recent charging period. For example,the first charging time point may be a start time point t4 of the recentcharging period, and the second charging time point may be an end timepoint t5 of the recent charging period.

In the step S7720, the external device 2000 may determine a second SOCchange, which is a difference between a third SOC at the firstdischarging time point and a fourth SOC at the second discharging timepoint, by applying the SOC estimation algorithm to the state parametersof each of the plurality of battery cells BC₁ to BC_(N), obtained duringthe discharging of the battery pack 10, for each battery cell BC. Thefirst discharging time point and the second discharging time point arenot particularly limited as long as they are two different time pointswithin the latest discharging period. For example, the first dischargingtime point may be a start time point t6 of the recent charging period,and the second discharging time point may be an end time point t7 of therecent charging period.

Referring to FIG. 18 , in an abnormal battery cell, the first SOC changeis a difference between the first SOC VC54 and the second SOC VC55, andthe second SOC change is a difference between the third SOC VC56 and thefourth SOC VC57.

In FIG. 37 , the step S7710 precedes the step S7720, but this should beunderstood as an example. For example, if the recent charging periodprecedes the recent discharging period, the step S7720 may precede thestep S7710. As another example, after both the recent charging periodand the recent discharging period end, the step S7710 and the step S7720may be performed simultaneously.

In the step S7730, the external device 2000 may determine an abnormalityfactor by dividing the first SOC change by the second SOC change foreach battery cell BC. That is, the abnormality factor may be determinedaccording to the formula of “abnormality factor=(first SOCchange)÷(second SOC change)”.

For example, referring to FIG. 18 , the abnormality factor of theabnormal battery cell may be determined according to the formula “{SOC(VC55)−SOC (VC54)}÷{SOC (VC56)−SOC (VC57)}. The abnormal factor may alsobe referred to as coulombic efficiency.

In the step S7740, the external device 2000 may determine a criterionfactor by applying a statistical algorithm to the abnormality factors ofat least two battery cells among the plurality of battery cells BC₁ toBC_(N).

The criterion factor may be equal to an average or a median value of theabnormality factors of at least two battery cells among the plurality ofbattery cells BC₁ to BC_(N). For example, referring to FIG. 18 , whenthe curve VC4 is an average SOC of the plurality of battery cells BC₁ toBC_(N), the criterion factor may be determined according to the formula“{SOC (VC45)−SOC (VC44)}+{SOC (VC46)−SOC (VC47)}”.

In the step S7750, the external device 2000 may detect an internal shortcircuit abnormality of the battery cell BC by comparing the abnormalityfactor with a criterion factor for each battery cell BC. In detectingthe internal short circuit abnormality, one or a combination of two ormore of the following detection conditions may be utilized.

-   -   [Condition #1: The abnormality factor must be smaller than the        criterion factor by a threshold value TH11 or more]    -   [Condition #2: The relative coulombic efficiency must be equal        to or less than a threshold value TH12, where TH12 is 0 to 1]    -   [Condition #3: The ratio of the abnormality factor to the        criterion factor must be smaller than the previous ratio by a        threshold value TH13 or more]

In Condition #2, the relative coulombic efficiency may be a ratio of theabnormality factor to the criterion factor, namely “abnormalityfactor+criterion factor”.

In Condition #3, the previous ratio is a ratio of the abnormality factorto the criterion factor based on the first SOCs in the charging period(t4 to t5 in FIG. 17 ) preceding the recent discharging period (t6 tot7) and the second SOCs in the discharging period (t2 to t3 in FIG. 17).

The threshold values TH11, TH12, TH13 may be predetermined values. As anexample, the threshold values TH11, TH12, TH13 may be the same as thepredetermined threshold values TH1, TH2, TH3 described above withreference to FIG. 36 .

The external device 2000 may determine at least one of the thresholdvalues TH11, TH12, TH13 based on the integration value of the chargingcurrent measured over the period from the first charging time point tothe second charging time point and the integration value of thedischarging current measured over the period from the first dischargingtime point to the second discharging time point.

Whenever the charging mode or discharging mode of the battery pack 10 isresumed, at least one of the threshold values TH11, TH12, TH13 may benewly updated. For example, the external device 2000 may obtain a targetvalue by dividing the integration value of the charging current by theintegration value of the discharging current. The external device 2000may determine at least one of the threshold values TH11, TH12, TH13 bymultiplying the ratio of the criterion factor to the target value by apredetermined scaling constant (which is a positive value). The scalingconstant used to determine any one of the threshold values TH11, TH12,TH13 may be different from the scaling constant used to determineanother one of the threshold values TH11, TH12, TH13. The target valuemay be determined during at least one of the steps S7710, S7720, S7730,and S7740. At least one of the threshold values TH1, TH2, TH3 may bedetermined during at least one of the steps S7730 and S7740.

When all of the plurality of battery cells BC₁ to BC_(N) are normal, thetarget value and the criterion factor may be substantially equal to eachother. Meanwhile, as the number of battery cells having an internalshort circuit abnormality among the plurality of battery cells BC₁ toBC_(N) increases, the criterion factor greatly decreases from the targetvalue. Accordingly, by determining at least one of the threshold valuesTH11, TH12, TH13 according to the above method, the accuracy ofdetecting an internal short circuit abnormality may be improved.

Meanwhile, after the target value is determined before the step S7740,in the step S7740, only abnormality factors less than or equal to thetarget value among all abnormality factors of the plurality of batterycells BC₁ to BC_(N) may be used to determine the criterion factor. Inthis case, in determining the criterion factor, since abnormalityfactors exceeding the target value are excluded from all the abnormalityfactors of the plurality of battery cells BC₁ to BC_(N), battery cellsBC having a relatively serious internal short circuit abnormality may bedetected preferentially from the plurality of battery cells BC₁ toBC_(N).

In each embodiment, when an internal short circuit abnormality isdetected in a predetermined number or more of battery cells among theplurality of battery cells BC₁ to BC_(N), the external device 2000 maygenerate second information representing that an internal short circuitabnormality is detected.

In each embodiment, the external device 2000 may reduce an allowablerange of the charging and discharging current when an internal shortcircuit abnormality is detected in a predetermined number or more ofbattery cells among the plurality of battery cells BC₁ to BC_(N). Forexample, the upper limit (positive value) of the allowable range maydecrease or the lower limit (negative value) of the allowable range mayincrease in proportion to the number of abnormal battery cell(s).

For example, the external device 2000 according to an embodiment of thepresent disclosure may be included in a diagnosing system for diagnosingabnormality of a battery cell. The diagnosing system may be operated inan electric vehicle repair shop, a battery manufacturer or a batterymaintenance company. For example, the diagnosing system may be used todiagnose abnormalities of battery cells loaded in electric vehicles orenergy storage systems, or may be used to diagnose abnormalities inbatteries of a newly developed model produced by a battery manufacturer.In particular, in the latter case, before commercializing the battery ofthe newly developed model, it is possible to check the state of thebattery by using the external device 2000.

As another example, the external device 2000 may be included in acontrol element of a system equipped with a battery.

In one example, the external device 2000 may be included in a controlsystem of an electric vehicle. In this case, the external device 2000may collect data on the charging capacity and discharging capacity ofthe battery cell in the process of charging and discharging the batterymounted in the electric vehicle, diagnose the state of the battery cellusing the collected data, and output the diagnosis results to theintegrated control display of the electric vehicle.

In another example, the external device 2000 may be included in thecontrol system of the energy storage system. In this case, the externaldevice 2000 may collect data on the charging capacity and dischargingcapacity of the battery cell during charging and discharging of theenergy storage system, diagnose the state of the battery cell using thecollected data, and output the diagnosis result through a display of anintegrated management computer accessible by the operator.

The user of the electric vehicle or the operator of the energy storagesystem may take appropriate safety measures when a diagnosis resultregarding lithium precipitation abnormality is output through thedisplay. In one example, the user of the electric vehicle may visit arepair shop and receive an inspection. In another example, the operatorof the energy storage system may replace the corresponding battery witha new battery.

In the present disclosure, the external device 2000 may optionallyinclude a processor, an application-specific integrated circuit (ASIC),another chipset, a logic circuit, a register, a communication modem, adata processing device, etc. known in the art to execute various controllogics described above. Also, when the control logic is implemented insoftware, the external device 2000 may be implemented as a set ofprogram modules. In this case, the program module may be stored in amemory and executed by a processor. The memory may be provided inside oroutside the processor, and may be connected to the processor by variouswell-known computer components. In addition, the memory may be includedin the storage unit 2100 of the present disclosure. In addition, thememory refers to a device in which information is stored regardless ofthe type of device, and does not refer to a specific memory device.

At least one or more of the various control logics of the externaldevice 2000 may be combined, and the combined control logics may bewritten in a computer-readable code scheme and recorded in acomputer-readable recording medium. The type of the recording medium isnot particularly limited as long as it can be accessed by the processorincluded in the computer. As an example, the recording medium includesat least one selected from the group consisting of ROM, RAM, registers,CD-ROM, magnetic tape, hard disk, floppy disk, and an optical datarecording device. In addition, the code scheme may be distributed,stored and executed on networked computers. In addition, functionalprograms, codes and code segments for implementing the combined controllogics may be easily deduced by programmers in the art to which thepresent disclosure pertains.

The present disclosure has been described in detail. However, it shouldbe understood that the detailed description and specific examples, whileindicating preferred embodiments of the disclosure, are given by way ofillustration only, since various changes and modifications within thescope of the disclosure will become apparent to those skilled in the artfrom this detailed description.

EXPLANATION OF REFERENCE SIGNS

-   -   1: battery cell diagnosing system    -   1000: battery cell diagnosing apparatus    -   2000: external device    -   100: current measuring unit    -   200: voltage sensing unit    -   300: data obtaining unit    -   400: first control unit    -   500: display unit    -   600: second control unit    -   10: battery pack

What is claimed is:
 1. A battery cell diagnosing apparatus, comprising:a current measuring unit configured to measure a current of a batterycell; a voltage sensing unit configured to sense a cell voltage of thebattery cell; and a first control unit configured to: transmit firstinformation of the battery cell including data obtained from the currentmeasuring unit and the voltage sensing unit to an external device,receive second information from the external device, wherein the secondinformation includes first diagnostic information of the battery cellobtained based on the first information, and derive second diagnosticinformation of the battery cell according to a diagnosis of an abnormalstate of the battery cell, wherein the second diagnostic information isdifferent from the first diagnostic information, and wherein thediagnosis of the abnormal state of the battery cell is based on at leastone of the first information or the second information.
 2. The batterycell diagnosing apparatus according to claim 1, wherein the firstdiagnostic information of the battery cell includes at least oneinformation among lithium precipitation diagnosis of the battery cell,abnormality of a parallel connection of the battery cell, and aninternal short circuit of the battery cell.
 3. The battery celldiagnosing apparatus according to claim 1, wherein the first controlunit is configured to display, on a display unit, information about anabnormality of the battery cell indicated by the first diagnosticinformation of the battery cell included in the second information. 4.The battery cell diagnosing apparatus according to claim 1, wherein thefirst control unit is configured to: detect at least one of a voltageabnormality of the battery cell and a behavior abnormality of thebattery cell based on the first information, and diagnose the abnormalstate of the battery cell based on at least one of the voltageabnormality, the behavior abnormality, and the second information. 5.The battery cell diagnosing apparatus according to claim 4, wherein thefirst control unit is configured to generate third informationrepresenting whether the battery cell is in the abnormal state based onat least one of the voltage abnormality, the behavior abnormality, andthe second information.
 6. The battery cell diagnosing apparatusaccording to claim 5, wherein the first control unit is configured todisplay the third information on a display unit.
 7. The battery celldiagnosing apparatus according to claim 5, wherein the first controlunit is configured to transmit the third information to a second controlunit of a device equipped with the battery cell.
 8. The battery celldiagnosing apparatus according to claim 1, wherein the first controlunit is configured to: generate time series data representing a historyof the cell voltage included in the first information over time,determine a first average cell voltage and a second average cell voltageof each battery cell based on the time series data, the first averagecell voltage being a short-term movement average, the second averagecell voltage being a long-term movement average, and detect a voltageabnormality of the battery cell based on a difference between the firstaverage cell voltage and the second average cell voltage.
 9. The batterycell diagnosing apparatus according to claim 8, wherein the battery celldiagnosing apparatus is configured to diagnose a plurality of batterycells, and wherein the first control unit is configured to: for each ofthe plurality of battery cells, determine a long-term and short-termaverage difference corresponding to the difference between the firstaverage cell voltage and the second average cell voltage, determine anaverage value of the long-term and short-term average differences of theplurality of battery cells, for each of the plurality of battery cells,determine a cell diagnosis deviation corresponding to a deviation of theaverage value of the long-term and short-term average differences andthe long-term and short-term average difference, and detect a batterycell that satisfies a condition in which the cell diagnosis deviationexceeds a diagnosis threshold as a voltage abnormal cell.
 10. Thebattery cell diagnosing apparatus according to claim 8, wherein thebattery cell diagnosing apparatus is configured to diagnose a pluralityof battery cells, and wherein the first control unit is configured to:for each of the plurality of battery cells, determine a long-term andshort-term average difference corresponding to the difference betweenthe first average cell voltage and the second average cell voltage,determine an average value of the long-term and short-term averagedifferences of the plurality of battery cells, for each of the pluralityof battery cells, determine a cell diagnosis deviation corresponding toa deviation of the average value of the long-term and short-term averagedifferences and the long-term and short-term average difference,determine a statistically variable threshold that depends on a standarddeviation of the cell diagnosis deviations of the plurality of batterycells, filter the time series data based on the statistically variablethreshold to generate filtered time series data; and detect a voltageabnormality of the battery cell based on the time or number of data ofthe filtered time series data exceeding a diagnosis threshold.
 11. Thebattery cell diagnosing apparatus according to claim 8, wherein thebattery cell diagnosing apparatus is configured to diagnose a pluralityof battery cells, and wherein the first control unit is configured to:for each of the plurality of battery cells, determine a long-term andshort-term average difference corresponding to the difference betweenthe first average cell voltage and the second average cell voltage,determine a normalization value corresponding to an average value of thelong-term and short-term average differences of the plurality of batterycells, for each of the plurality of battery cells, normalize thelong-term and short-term average difference according to thenormalization value, determine a statistically variable threshold thatdepends on a standard deviation of the normalized cell diagnosisdeviations of the plurality of battery cells, for each of the pluralityof battery cells, filter the normalized long-term and short-term averagedifference of each battery cell based on the statistically variablethreshold to generate filtered time series data; and detect a voltageabnormality of the battery cell based on the time or number of data ofthe filtered time series data exceeding a diagnosis threshold.
 12. Thebattery cell diagnosing apparatus according to claim 1, wherein thefirst control unit is configured to: determine a plurality of subvoltage curves by applying a moving window of a first time length to atime series of the cell voltage included in the first information,determine a long-term average voltage value of each sub voltage curveusing a first average filter of the first time length, determine ashort-term average voltage value of each sub voltage curve using asecond average filter of a second time length shorter than the firsttime length, determine a voltage deviation corresponding to a differencebetween the long-term average voltage value and the short-term averagevoltage value of each sub voltage curve, and compare each of theplurality of voltage deviations determined for the plurality of subvoltage curves with at least one of a first threshold deviation and asecond threshold deviation in order to detect the behavior abnormalityof the battery cell.
 13. The battery cell diagnosing apparatus accordingto claim 12, wherein the first control unit is configured to detect thebehavior abnormality corresponding to two voltage deviations thatrespectively satisfy a first condition, a second condition and a thirdcondition among the plurality of voltage deviations, wherein the firstcondition is satisfied when a first voltage deviation of the two voltagedeviations is equal to or greater than the first threshold deviation,wherein the second condition is satisfied when a second voltagedeviation of the two voltage deviations is equal to or less than thesecond threshold deviation, and wherein the third condition is satisfiedwhen a time interval between the two voltage deviations is equal to orless than a threshold time.
 14. The battery cell diagnosing apparatusaccording to claim 1, wherein the second information represents whetheran accumulated capacity difference change amount is greater than orequal to a threshold value, the accumulated capacity difference changeamount is the sum of capacity difference change amounts, each of thecapacity difference change amounts is a difference between a capacitydifference of a k^(th) charging and discharging cycle of the batterycell and a capacity difference of a k−1^(th) charging and dischargingcycle of the battery cell, the k is a natural number greater than orequal to 2, the capacity difference of each charging and dischargingcycle corresponds to a difference between a charging capacity of thebattery cell during a charging process of the charging and dischargingcycle and a discharging capacity of the battery cell during adischarging process of the charging and discharging cycle, and each ofthe charging capacity and the discharging capacity is derivable fromdata obtained from the current measuring unit and included in the firstinformation.
 15. The battery cell diagnosing apparatus according toclaim 1, wherein the second information represents a capacity differencechange amount between successive charging and discharging cycles of thebattery cell, and a capacity difference for each charging anddischarging cycle of the battery cell is a difference between (i) acharging capacity of the battery cell during a charging process of thecharging and discharging cycle of the battery cell and (ii) adischarging capacity of the battery cell during a discharging process ofthe charging and discharging cycle of the battery cell.
 16. The batterycell diagnosing apparatus according to claim 1, wherein the secondinformation represents whether a parallel connection of a plurality ofunit cells included in the battery cell is abnormal based on a result ofmonitoring a change over time of an estimated capacity value by theexternal device, the estimated capacity value represents a full chargingcapacity of the battery cell based on charging and discharging data, andthe charging and discharging data includes a voltage time seriesrepresenting a change over time of the voltage of the battery cell and acurrent time series representing a change over time of the charging anddischarging current of the battery cell.
 17. The battery cell diagnosingapparatus according to claim 1, wherein the second informationrepresents whether the battery cell has an internal short circuit basedon a first SOC change and a criterion factor of the battery cell, thecriterion factor is determined by applying a statistical algorithm tothe first SOC change of at least two battery cells among a plurality ofbattery cells, the first SOC change is a difference between a first SOCat a first charging time point of each battery cell and a second SOC ata second charging time point, the first SOC is estimated by applying aSOC estimation algorithm to a state parameter of the battery cell at thefirst charging time point, the second SOC is estimated by applying theSOC estimation algorithm to the state parameter of the battery cell atthe second charging time point, and the state parameter is obtainedbased on the first information.
 18. A battery cell diagnosing system,comprising the battery cell diagnosing apparatus according to claim 1,and the external device, wherein the external device is configured toderive the second information based on at least a part of the firstinformation.
 19. A battery cell diagnosing method, comprising: by acontrol unit, obtaining data including at least one of a chargingcurrent and a discharging current of a battery cell, and a cell voltageof the battery cell; by the control unit, transmitting first informationof the battery cell including the obtained data to an external device;by the control unit, receiving second information from the externaldevice, wherein the second information includes first diagnosticinformation of the battery cell obtained based on the first information;and by the control unit, deriving second diagnostic information of thebattery cell according to a diagnosis of an abnormal state of thebattery cell, wherein the second diagnostic information is differentfrom the first diagnostic information, and wherein the diagnosis of theabnormal state of the battery cell is based on at least one of the firstinformation or the second information.
 20. A battery cell diagnosingapparatus, comprising: a current measuring unit configured to measure acurrent of a battery cell; a voltage sensing unit configured to sense acell voltage of the battery cell; and a first control unit configuredto: transmit first information of the battery cell including dataobtained from at least one of the current measuring unit or the voltagesensing unit to an external device, receive second information from theexternal device, wherein the second information includes firstdiagnostic information of the battery cell obtained based on the firstinformation, derive second diagnostic information of the battery cellbased on the first information, wherein the second diagnosticinformation is different from the first diagnostic information, anddiagnose an abnormal state of the battery cell based on the firstdiagnostic information and the second diagnostic information.